{ "id": "AIBD-0352", "title": "EasyScan GO machine-learning malaria microscopy reaches 91.1% sensitivity and 75.6% specificity against expert microscopy at 11 sites; accuracy study only, manufacturer-funded", "summary": "Das et al. 2022: 2,250 blood films at 11 sites in 11 countries (2018–2019); EasyScan GO sensitivity 91.1%, specificity 75.6% vs expert microscopy; no patient outcome; Global Good funded.", "date_observed": "2022-04-12", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "A multi-center observational study was published in Malaria Journal on 12 April 2022 (Das et al.). The fieldwork is dated only to 2018 and 2019. It evaluated the EasyScan GO, a digital microscopy device whose convolutional-neural-network software was developed by Intellectual Ventures' Global Good Fund. Motic built its production prototype. The 11 sites were in 11 countries: Burkina Faso, Kenya, Republic of the Congo, Senegal, South Africa, Uganda, Bangladesh, Cambodia, Nepal, Thailand and Brazil. In total 2,250 Giemsa-stained blood films were read independently by expert microscopists and by the device. Diagnostic sensitivity was 91.1% (95% CI 88.9–92.7) and specificity 75.6% (73.1–78.0); with good-quality slides specificity rose to 85.1%. Species were identified accurately in 93% of P. falciparum and 92% of P. vivax samples. Parasite density estimates were within 25% of the reference in 23% of slides. The authors conclude the device meets WHO-TDR level 2 criteria for detection and species identification and level 4 for quantification and false-positive rate. They say all parameters were significantly affected by slide quality, and the study did not evaluate performance under routine field operation. The study was funded by the Global Good Fund, which donated the devices; six co-authors were its employees. No patient-management outcome was measured.", "category": "general benefit", "unexpected": false, "purpose": "Automated detection, species identification and quantification of malaria parasites in blood films to replace or support expert microscopy. The evaluated benefit (diagnostic accuracy) is that purpose.", "system": "EasyScan GO digital malaria microscopy device with convolutional-neural-network image analysis of thick blood films", "model_version": "version1.15.17b", "developer": "Intellectual Ventures' Global Good Fund (software and original hardware); Motic (production prototype device, software licensed to Motic)", "deployer": "Research sites in 11 countries under a multi-center study with University of Oxford / Mahidol-Oxford Tropical Medicine Research Unit-affiliated investigators (research use, not routine service)", "location": "11 sites in Burkina Faso, Kenya, Republic of the Congo, Senegal, South Africa, Uganda, Bangladesh, Cambodia, Nepal, Thailand and Brazil", "domain": "Malaria diagnostics / digital microscopy", "segment": "Global-health diagnostic research", "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The device matched rather than exceeded expert microscopy (91.1% sensitivity, 75.6% specificity), and no patient outcome was tracked.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No patient-management or clinical outcome was measured." }, { "group": "organizational", "depth": null, "reason": "Laboratory-level accuracy was measured under study conditions, not routine field operation." }, { "group": "societal", "depth": null, "reason": "No deployment at program scale is documented." } ], "ai_role": { "status": "assessed", "statement": "The machine-learning device produced the readings compared against expert microscopy, so the measured accuracy is the AI's own performance. The evidence is a single peer-reviewed study funded by the maker (Global Good Fund) with maker-employed co-authors who provided inputs to study design and training. The authors state the study did not evaluate performance under routine field operation and that all parameters were significantly affected by slide quality. No independent or outcome-level evidence was found." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Das, D., Vongpromek, R., Assawariyathipat, T., et al. (2022). Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malaria Journal 21, 122. doi:10.1186/s12936-022-04146-1.", "title": "Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning", "url": "https://doi.org/10.1186/s12936-022-04146-1", "date": "2022-04-12", "written_by": "makers", "quote": "The diagnostic sensitivity of EasyScan Go was 91.1% (95% CI 88.9-92.7), and specificity 75.6% (95% CI 73.1-78.0)." }, { "kind": "peer-reviewed study", "citation": "Das, D., et al. (2022). Same article, full text (methods and limitations). Europe PMC PMC9004086.", "title": "Field evaluation of the diagnostic performance of EasyScan GO (full text)", "url": "https://europepmc.org/article/PMC/PMC9004086", "date": "2022-04-12", "written_by": "makers", "quote": "In addition, the study did not evaluate the performance of the EasyScan Go under routine field operation." }, { "kind": "peer-reviewed study", "citation": "Das, D., et al. (2022). Same article, competing interests statement. Europe PMC PMC9004086.", "title": "Field evaluation of the diagnostic performance of EasyScan GO (competing interests)", "url": "https://europepmc.org/article/PMC/PMC9004086", "date": "2022-04-12", "written_by": "makers", "quote": "MPH, SKM, CM, SB, CBD and CB were employees of the Global Good Fund, Intellectual Ventures at the time of study implementation" }, { "kind": "peer-reviewed study", "citation": "Das, D., Vongpromek, R., Assawariyathipat, T., et al. (2022). Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malaria Journal 21, 122. doi:10.1186/s12936-022-04146-1. Full text as served by the Europe PMC REST API (PMC9004086).", "title": "Field evaluation of the diagnostic performance of EasyScan GO (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML", "date": "2022-04-12", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "EasyScan GO devices donated and set up with manufacturer training at each site", "Giemsa-stained thick blood films read in parallel by two expert microscopists as reference", "STARD-reported multi-center observational design across 11 countries (ClinicalTrials.gov NCT03512678)" ], "must_hold": [ "Good slide quality, which the authors found significantly affected all performance parameters", "Parasitemia above about 200 parasites/uL, below which sensitivity fell to 57%", "Thick-film-only software (the tested version did not separate non-falciparum species)" ], "echoes": [ { "date": null, "setting": "Earlier field test of the first thick-film version in Thailand, 2014–2015, reported in the same article's background", "model_version": "first thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" }, { "date": null, "setting": "Thailand and Indonesia, 2016–2017: the article reports sensitivity 89% and specificity 97% for the second thick-film algorithm against expert microscopy", "model_version": "second thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" } ] }, "paired_incident": null, "headline": "EasyScan GO reaches 91.1% sensitivity for malaria at 11 sites", "metric": "Diagnostic sensitivity was 91.1% (95% CI 88.9–92.7) and specificity 75.6% (73.1–78.0)", "comparator": { "against": "expert microscopy", "before": null, "after": "diagnostic sensitivity of the EasyScan Go device was 91.1% (95% CI 88.9–92.7) and specificity was 75.6% (95% CI 73.1–78.0)", "basis": "measured", "quote": "Compared to reference microscopy, the diagnostic sensitivity of the EasyScan Go device was 91.1% (95% CI 88.9–92.7) and specificity was 75.6% (95% CI 73.1–78.0) (Table 1).", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML" }, "later": { "status": "unknown", "date": null, "note": "No later evaluation, approval or deployment is reported in the article.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Intellectual_Ventures", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: three peer-reviewed studies from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0352", "catalog_version": "1.0.0", "source_fingerprint": "165d995b2ec7578feda05d5f3990957643789fa9131b19484d3c70473c63db0f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0352-source-0001", "AIBD-0352-source-0002", "AIBD-0352-source-0003", "AIBD-0352-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Intellectual Ventures' Global Good Fund (software and original hardware); Motic (production prototype device, software licensed to Motic)", "deployer": "Research sites in 11 countries under a multi-center study with University of Oxford / Mahidol-Oxford Tropical Medicine Research Unit-affiliated investigators (research use, not routine service)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The machine-learning device produced the readings compared against expert microscopy, so the measured accuracy is the AI's own performance. The evidence is a single peer-reviewed study funded by the maker (Global Good Fund) with maker-employed co-authors who provided inputs to study design and training. The authors state the study did not evaluate performance under routine field operation and that all parameters were significantly affected by slide quality. No independent or outcome-level evidence was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "general benefit", "ai_role": { "status": "assessed", "statement": "The machine-learning device produced the readings compared against expert microscopy, so the measured accuracy is the AI's own performance. The evidence is a single peer-reviewed study funded by the maker (Global Good Fund) with maker-employed co-authors who provided inputs to study design and training. The authors state the study did not evaluate performance under routine field operation and that all parameters were significantly affected by slide quality. No independent or outcome-level evidence was found." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The makers' claim", "basis": "Sources: three peer-reviewed studies from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No patient-management or clinical outcome was measured." }, { "group": "organizational", "reason": "Laboratory-level accuracy was measured under study conditions, not routine field operation." }, { "group": "societal", "reason": "No deployment at program scale is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No patient-management or clinical outcome was measured." }, { "group": "organizational", "reason": "Laboratory-level accuracy was measured under study conditions, not routine field operation." }, { "group": "societal", "reason": "No deployment at program scale is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "Diagnostic sensitivity was 91.1% (95% CI 88.9–92.7) and specificity 75.6% (73.1–78.0)", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "No later evaluation, approval or deployment is reported in the article." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0352" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Task Accuracy", "taxonomy_note": "The device matched rather than exceeded expert microscopy (91.1% sensitivity, 75.6% specificity), and no patient outcome was tracked." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "A multi-center observational study was published in Malaria Journal on 12 April 2022 (Das et al.). The fieldwork is dated only to 2018 and 2019. It evaluated the EasyScan GO, a digital microscopy device whose convolutional-neural-network software was developed by Intellectual Ventures' Global Good Fund. Motic built its production prototype. The 11 sites were in 11 countries: Burkina Faso, Kenya, Republic of the Congo, Senegal, South Africa, Uganda, Bangladesh, Cambodia, Nepal, Thailand and Brazil. In total 2,250 Giemsa-stained blood films were read independently by expert microscopists and by the device. Diagnostic sensitivity was 91.1% (95% CI 88.9–92.7) and specificity 75.6% (73.1–78.0); with good-quality slides specificity rose to 85.1%. Species were identified accurately in 93% of P. falciparum and 92% of P. vivax samples. Parasite density estimates were within 25% of the reference in 23% of slides. The authors conclude the device meets WHO-TDR level 2 criteria for detection and species identification and level 4 for quantification and false-positive rate. They say all parameters were significantly affected by slide quality, and the study did not evaluate performance under routine field operation. The study was funded by the Global Good Fund, which donated the devices; six co-authors were its employees. No patient-management outcome was measured.", "benefit_type": "Improved Task Accuracy" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "No patient-management or clinical outcome was measured." }, { "group": "organizational", "reason": "Laboratory-level accuracy was measured under study conditions, not routine field operation." }, { "group": "societal", "reason": "No deployment at program scale is documented." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Automated detection, species identification and quantification of malaria parasites in blood films to replace or support expert microscopy. The evaluated benefit (diagnostic accuracy) is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "11 sites in Burkina Faso, Kenya, Republic of the Congo, Senegal, South Africa, Uganda, Bangladesh, Cambodia, Nepal, Thailand and Brazil", "developer_country": "United States" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Automated detection, species identification and quantification of malaria parasites in blood films to replace or support expert microscopy. The evaluated benefit (diagnostic accuracy) is that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2022-04-12", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Intellectual_Ventures", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "EasyScan GO devices donated and set up with manufacturer training at each site", "Giemsa-stained thick blood films read in parallel by two expert microscopists as reference", "STARD-reported multi-center observational design across 11 countries (ClinicalTrials.gov NCT03512678)" ], "must_hold": [ "Good slide quality, which the authors found significantly affected all performance parameters", "Parasitemia above about 200 parasites/uL, below which sensitivity fell to 57%", "Thick-film-only software (the tested version did not separate non-falciparum species)" ], "echoes": [ { "setting": "Earlier field test of the first thick-film version in Thailand, 2014–2015, reported in the same article's background", "model_version": "first thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" }, { "setting": "Thailand and Indonesia, 2016–2017: the article reports sensitivity 89% and specificity 97% for the second thick-film algorithm against expert microscopy", "model_version": "second thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "EasyScan GO devices donated and set up with manufacturer training at each site", "Giemsa-stained thick blood films read in parallel by two expert microscopists as reference", "STARD-reported multi-center observational design across 11 countries (ClinicalTrials.gov NCT03512678)" ], "must_hold": [ "Good slide quality, which the authors found significantly affected all performance parameters", "Parasitemia above about 200 parasites/uL, below which sensitivity fell to 57%", "Thick-film-only software (the tested version did not separate non-falciparum species)" ], "echoes": [ { "setting": "Earlier field test of the first thick-film version in Thailand, 2014–2015, reported in the same article's background", "model_version": "first thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" }, { "setting": "Thailand and Indonesia, 2016–2017: the article reports sensitivity 89% and specificity 97% for the second thick-film algorithm against expert microscopy", "model_version": "second thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Intellectual_Ventures", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Malaria diagnostics / digital microscopy", "segment": "Global-health diagnostic research" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Das, D., Vongpromek, R., Assawariyathipat, T., et al. (2022). Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malaria Journal 21, 122. doi:10.1186/s12936-022-04146-1.", "title": "Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning", "url": "https://doi.org/10.1186/s12936-022-04146-1", "date": "2022-04-12", "written_by": "makers", "quote": "The diagnostic sensitivity of EasyScan Go was 91.1% (95% CI 88.9-92.7), and specificity 75.6% (95% CI 73.1-78.0)." }, { "kind": "peer-reviewed study", "citation": "Das, D., et al. (2022). Same article, full text (methods and limitations). Europe PMC PMC9004086.", "title": "Field evaluation of the diagnostic performance of EasyScan GO (full text)", "url": "https://europepmc.org/article/PMC/PMC9004086", "date": "2022-04-12", "written_by": "makers", "quote": "In addition, the study did not evaluate the performance of the EasyScan Go under routine field operation." }, { "kind": "peer-reviewed study", "citation": "Das, D., et al. (2022). Same article, competing interests statement. Europe PMC PMC9004086.", "title": "Field evaluation of the diagnostic performance of EasyScan GO (competing interests)", "url": "https://europepmc.org/article/PMC/PMC9004086", "date": "2022-04-12", "written_by": "makers", "quote": "MPH, SKM, CM, SB, CBD and CB were employees of the Global Good Fund, Intellectual Ventures at the time of study implementation" }, { "kind": "peer-reviewed study", "citation": "Das, D., Vongpromek, R., Assawariyathipat, T., et al. (2022). Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malaria Journal 21, 122. doi:10.1186/s12936-022-04146-1. Full text as served by the Europe PMC REST API (PMC9004086).", "title": "Field evaluation of the diagnostic performance of EasyScan GO (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML", "date": "2022-04-12", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0352-source-0001", "AIBD-0352-source-0002", "AIBD-0352-source-0003", "AIBD-0352-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Das, D., Vongpromek, R., Assawariyathipat, T., et al. (2022). Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malaria Journal 21, 122. doi:10.1186/s12936-022-04146-1.", "title": "Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning", "url": "https://doi.org/10.1186/s12936-022-04146-1", "date": "2022-04-12", "written_by": "makers", "quote": "The diagnostic sensitivity of EasyScan Go was 91.1% (95% CI 88.9-92.7), and specificity 75.6% (95% CI 73.1-78.0)." }, { "kind": "peer-reviewed study", "citation": "Das, D., et al. (2022). Same article, full text (methods and limitations). Europe PMC PMC9004086.", "title": "Field evaluation of the diagnostic performance of EasyScan GO (full text)", "url": "https://europepmc.org/article/PMC/PMC9004086", "date": "2022-04-12", "written_by": "makers", "quote": "In addition, the study did not evaluate the performance of the EasyScan Go under routine field operation." }, { "kind": "peer-reviewed study", "citation": "Das, D., et al. (2022). Same article, competing interests statement. Europe PMC PMC9004086.", "title": "Field evaluation of the diagnostic performance of EasyScan GO (competing interests)", "url": "https://europepmc.org/article/PMC/PMC9004086", "date": "2022-04-12", "written_by": "makers", "quote": "MPH, SKM, CM, SB, CBD and CB were employees of the Global Good Fund, Intellectual Ventures at the time of study implementation" }, { "kind": "peer-reviewed study", "citation": "Das, D., Vongpromek, R., Assawariyathipat, T., et al. (2022). Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malaria Journal 21, 122. doi:10.1186/s12936-022-04146-1. Full text as served by the Europe PMC REST API (PMC9004086).", "title": "Field evaluation of the diagnostic performance of EasyScan GO (Europe PMC full text XML)", "url": "https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9004086/fullTextXML", "date": "2022-04-12", "written_by": "makers" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Das et al. 2022: 2,250 blood films at 11 sites in 11 countries (2018–2019); EasyScan GO sensitivity 91.1%, specificity 75.6% vs expert microscopy; no patient outcome; Global Good funded.", "account": "A multi-center observational study was published in Malaria Journal on 12 April 2022 (Das et al.). The fieldwork is dated only to 2018 and 2019. It evaluated the EasyScan GO, a digital microscopy device whose convolutional-neural-network software was developed by Intellectual Ventures' Global Good Fund. Motic built its production prototype. The 11 sites were in 11 countries: Burkina Faso, Kenya, Republic of the Congo, Senegal, South Africa, Uganda, Bangladesh, Cambodia, Nepal, Thailand and Brazil. In total 2,250 Giemsa-stained blood films were read independently by expert microscopists and by the device. Diagnostic sensitivity was 91.1% (95% CI 88.9–92.7) and specificity 75.6% (73.1–78.0); with good-quality slides specificity rose to 85.1%. Species were identified accurately in 93% of P. falciparum and 92% of P. vivax samples. Parasite density estimates were within 25% of the reference in 23% of slides. The authors conclude the device meets WHO-TDR level 2 criteria for detection and species identification and level 4 for quantification and false-positive rate. They say all parameters were significantly affected by slide quality, and the study did not evaluate performance under routine field operation. The study was funded by the Global Good Fund, which donated the devices; six co-authors were its employees. No patient-management outcome was measured." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The network analyzed microscope images for malaria parasites." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The deployment used software version 1.15.17b." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "A convolutional neural network that analyzes thick blood films." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "version1.15.17b" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Version 1.15.17b.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "EasyScan GO digital malaria microscopy device with convolutional-neural-network image analysis of thick blood films" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Intellectual Ventures' Global Good Fund (software and original hardware); Motic (production prototype device, software licensed to Motic)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "EasyScan GO machine-learning malaria microscopy reaches 91.1% sensitivity and 75.6% specificity against expert microscopy at 11 sites; accuracy study only, manufacturer-funded", "headline": "EasyScan GO reaches 91.1% sensitivity for malaria at 11 sites" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "165d995b2ec7578feda05d5f3990957643789fa9131b19484d3c70473c63db0f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "A multi-center observational study was published in Malaria Journal on 12 April 2022 (Das et al.). The fieldwork is dated only to 2018 and 2019. It evaluated the EasyScan GO, a digital microscopy device whose convolutional-neural-network software was developed by Intellectual Ventures' Global Good Fund. Motic built its production prototype. The 11 sites were in 11 countries: Burkina Faso, Kenya, Republic of the Congo, Senegal, South Africa, Uganda, Bangladesh, Cambodia, Nepal, Thailand and Brazil. In total 2,250 Giemsa-stained blood films were read independently by expert microscopists and by the device. Diagnostic sensitivity was 91.1% (95% CI 88.9–92.7) and specificity 75.6% (73.1–78.0); with good-quality slides specificity rose to 85.1%. Species were identified accurately in 93% of P. falciparum and 92% of P. vivax samples. Parasite density estimates were within 25% of the reference in 23% of slides. The authors conclude the device meets WHO-TDR level 2 criteria for detection and species identification and level 4 for quantification and false-positive rate. They say all parameters were significantly affected by slide quality, and the study did not evaluate performance under routine field operation. The study was funded by the Global Good Fund, which donated the devices; six co-authors were its employees. No patient-management outcome was measured.", "replication": { "produced_by": [ "EasyScan GO devices donated and set up with manufacturer training at each site", "Giemsa-stained thick blood films read in parallel by two expert microscopists as reference", "STARD-reported multi-center observational design across 11 countries (ClinicalTrials.gov NCT03512678)" ], "must_hold": [ "Good slide quality, which the authors found significantly affected all performance parameters", "Parasitemia above about 200 parasites/uL, below which sensitivity fell to 57%", "Thick-film-only software (the tested version did not separate non-falciparum species)" ], "echoes": [ { "setting": "Earlier field test of the first thick-film version in Thailand, 2014–2015, reported in the same article's background", "model_version": "first thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" }, { "setting": "Thailand and Indonesia, 2016–2017: the article reports sensitivity 89% and specificity 97% for the second thick-film algorithm against expert microscopy", "model_version": "second thick-film algorithm", "outcome": "recurred", "source": "https://europepmc.org/article/PMC/PMC9004086" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 32, "reviewed": 0, "record_mapped": 32, "not_assessed": 59, "complete": false, "by_state": { "value_present": 32, "not_reported": 0, "unresolved": 0, "not_assessed": 59, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }