<scp>The point‐of‐care</scp> use of a facial phenotyping tool in the genetics clinic: Enhancing diagnosis and education with machine learning
Bibliographic record
Abstract
Computer-assisted pattern recognition platforms, such as Face2Gene® (F2G), can facilitate the diagnosis of children with rare genetic syndromes by comparing a patient's features to known genetic diagnoses. Our work designed, implemented, and evaluated an innovative model of care in clinical genetics in a heterogeneous and multicultural patient population that utilized this facial phenotyping software at the point-of-care. We assessed the performance of F2G by comparing the suggested diagnoses to the patient's confirmed molecular diagnosis. Providers' overall experiences with the technology and trainees' educational experiences were assessed with questionnaires. We achieved an overall diagnostic yield of 57%. This increased to 82% when cases diagnosed with syndromes not recognized by F2G were removed. The mean rank of a confirmed diagnosis in the top 10 was 2.3 (CI 1.5-3.2) and the mean gestalt score 37.6%. The most commonly suggested diagnoses were Noonan syndrome, mucopolysaccharidosis, and 22q11.2 deletion syndrome. Our qualitative assessment revealed that clinicians and trainees saw value using the tool in practice. Overall, this work helped to implement an innovative patient care delivery model in clinical genetics that utilizes a facial phenotyping tool at the point-of-care. Our data suggest that F2G has utility in the genetics clinic as a clinical decision support tool in diverse populations, with a majority of patients having their eventual diagnosis listed in the top 10 suggested syndromes based on a photograph alone. It shows promise for further integration into clinical care and medical education, and we advocate for its continued use, adoption and refinement along with transparent and accountable industrial partnerships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".