{
  "id": "AIBD-0037",
  "title": "Face2Gene redirects an 18-year misdiagnosis of Cornelia de Lange syndrome toward DYRK1A-related MRD7 in a 22-year-old man",
  "summary": "Face2Gene suggested MRD7 for a 22-year-old misdiagnosed with Cornelia de Lange at age 4; exome sequencing confirmed a DYRK1A variant.",
  "date_observed": "2023-01-05",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "Obara, Abe and Toyoshima of the National Hospital Organization Akita National Hospital, Japan, reported a 22-year-old man in Cureus on 5 January 2023. He had been diagnosed with Cornelia de Lange syndrome at age four based on facial dysmorphism, microcephaly, short stature and intellectual disability. At 22 his Cornelia de Lange clinical diagnostic score was 5 of 19. His frontal facial photograph and clinical features were uploaded to Face2Gene. This “introduced the following candidate diseases: Angelman syndrome (AS, OMIM 105833), Witteveen-Kolk syndrome (WKOS; MIM 613406), Rubinstein-Taybi syndrome (RTS; OMIM 180849), and MRD7”. The paper gives no ranking or scores for these candidates. Whole-exome sequencing then identified a novel heterozygous DYRK1A variant, c.848dup, p.(Asn283LysfsTer6), confirming MRD7. The 18-year span in the title derives from the ages the paper gives, diagnosed at four and re-evaluated at 22. The paper does not state a duration. The consultation date is not given.",
  "category": "benefit created",
  "unexpected": false,
  "purpose": "Face2Gene is built to suggest candidate syndromes from facial images and clinical features. This suggestion was that purpose.",
  "system": "Face2Gene (DeepGestalt facial phenotyping)",
  "model_version": null,
  "developer": "FDNA",
  "deployer": "Department of Neurology, National Hospital Organization Akita National Hospital",
  "location": "Yurihonjo, Japan",
  "domain": "Healthcare / clinical genetics",
  "segment": "Rare disease diagnosis",
  "benefit_type": "Improved Physical Health",
  "taxonomy_note": null,
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  "ratings": [
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      "group": "personal",
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    "statement": "Face2Gene listed MRD7 among four candidates. The paper gives no ranking. Whole-exome sequencing made the diagnosis. The AI redirected the differential after the clinical score undermined the prior diagnosis."
  },
  "evidence": [
    {
      "kind": "case report",
      "citation": "Obara K, Abe E, Toyoshima I (2023). Whole-Exome Sequencing Identified a Novel DYRK1A Variant in a Patient With Intellectual Developmental Disorder, Autosomal Dominant 7. Cureus (PMC9822525).",
      "title": "Whole-Exome Sequencing Identified a Novel DYRK1A Variant in a Patient With Intellectual Developmental Disorder, Autosomal Dominant 7",
      "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9822525/",
      "date": "2023-01-05",
      "written_by": "deployer",
      "quote": "As a result, Face2Gene introduced the following candidate diseases: Angelman syndrome (AS, OMIM 105833), Witteveen-Kolk syndrome (WKOS; MIM 613406), Rubinstein-Taybi syndrome (RTS; OMIM 180849), and MRD7."
    }
  ],
  "replication": {
    "produced_by": [
      "Frontal facial photograph plus clinical features uploaded to Face2Gene",
      "A prior clinical diagnosis that no longer fit the clinical score",
      "Access to whole-exome sequencing to confirm"
    ],
    "must_hold": [
      "The true syndrome is represented in Face2Gene's training data (authors note MRD7 recommendations improve as cases are registered)",
      "Clinicians pursue a candidate that is one of several suggestions"
    ],
    "echoes": [
      {
        "date": "2025-11-01",
        "setting": "International case series, 17 cases / 19 patients, in which DeepGestalt changed geneticists' testing decisions; American Journal of Medical Genetics Part A vol. 197 issue 11 (issue month, first day used)",
        "model_version": null,
        "outcome": "recurred",
        "source": "Rudy NL, Gomes A, et al. (2025). Artificial Intelligence Software Changes Rare Disease Testing Strategy in Real Time. Am J Med Genet A, https://doi.org/10.1002/ajmg.a.64171 (quote: 'We present 17 cases involving 19 patients')"
      }
    ]
  },
  "paired_incident": {
    "database": "AI Incident Database",
    "identifier": "582",
    "title": "Racial Bias in Lung Function Diagnostic Algorithm Leads to Underdiagnosis in Black Men",
    "url": "https://incidentdatabase.ai/cite/582",
    "severity": null,
    "group": "personal",
    "basis": "Clinician-facing diagnostic algorithm: Face2Gene redirected an 18-year misdiagnosis, while a race-adjusted lung-function diagnostic algorithm led to inaccurate or delayed diagnoses for Black men at the University of Pennsylvania Health System."
  },
  "headline": "Face2Gene suggests MRD7 after an 18-year misdiagnosis",
  "metric": null,
  "comparator": {
    "against": "the clinical diagnosis of Cornelia de Lange syndrome made at age four",
    "before": "At four years of age, he was clinically diagnosed with Cornelia de Lange syndrome (CdLS; OMIM 122470, 300590, 300882, 610759, and 614701) due to his facial dysmorphism, microcephaly, short stature, and intellectual disability.",
    "after": "Face2Gene introduced the following candidate diseases: Angelman syndrome (AS, OMIM 105833), Witteveen-Kolk syndrome (WKOS; MIM 613406), Rubinstein-Taybi syndrome (RTS; OMIM 180849), and MRD7.",
    "basis": "measured",
    "quote": "As a result, Face2Gene introduced the following candidate diseases: Angelman syndrome (AS, OMIM 105833), Witteveen-Kolk syndrome (WKOS; MIM 613406), Rubinstein-Taybi syndrome (RTS; OMIM 180849), and MRD7.",
    "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9822525/"
  },
  "later": {
    "status": "unknown",
    "date": null,
    "note": "No later development found.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": "United States",
  "deployer_country": "Japan",
  "unrated_because": null,
  "brief_notes": null,
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    {
      "date": "2026-10-20",
      "change": "Record drafted from the cited sources.",
      "source": null,
      "editor": "AIBD"
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    {
      "date": "2026-10-20",
      "change": "Paired with AI Incident Database incident 582 (medium confidence); severity not given by the source.",
      "source": "https://incidentdatabase.ai/cite/582",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-20",
      "change": "Restored the OMIM/MIM identifiers to the Face2Gene candidate-list quote in the evidence and the account so that it is verbatim, replaced the unsupported “not ranked first” and “non-top-ranked suggestion” wording with a statement that the paper gives no ranking, and noted in the account that the title's 18-year span is derived from the stated ages of four and 22 because the paper gives no duration.",
      "source": "Obara K, Abe E, Toyoshima I (2023) Cureus, PMC9822525 (full text checked via Europe PMC): https://pmc.ncbi.nlm.nih.gov/articles/PMC9822525/",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Added what the benefit is measured against from the cited sources.",
      "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9822525/",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Benefit type set to Improved Physical Health 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"
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    {
      "date": "2026-10-21",
      "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://fdna.com/privacy-policy/; https://akita.hosp.go.jp/",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
      "source": null,
      "editor": "AIBD"
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  "confidence": "claimed",
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                {
                  "date": "2025-11-01",
                  "setting": "International case series, 17 cases / 19 patients, in which DeepGestalt changed geneticists' testing decisions; American Journal of Medical Genetics Part A vol. 197 issue 11 (issue month, first day used)",
                  "outcome": "recurred",
                  "source": "Rudy NL, Gomes A, et al. (2025). Artificial Intelligence Software Changes Rare Disease Testing Strategy in Real Time. Am J Med Genet A, https://doi.org/10.1002/ajmg.a.64171 (quote: 'We present 17 cases involving 19 patients')"
                }
              ]
            }
          }
        },
        "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": 31,
      "reviewed": 0,
      "record_mapped": 31,
      "not_assessed": 60,
      "complete": false,
      "by_state": {
        "value_present": 31,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 60,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}
