The Diagnostic Performance of Expert Dermoscopists vs a Computer-Vision System on Small-Diameter Melanomas
Bibliographic record
Abstract
OBJECTIVE: To evaluate the performance of dermoscopists in diagnosing small pigmented skin lesions (diameter </= 6 mm) compared with an automatic multispectral computer-vision system. DESIGN: Blinded comparison study. SETTING: Dermatologic hospital-based clinics and private practice offices. Patients From a computerized skin imaging database of 990 small (</= 6-mm) pigmented skin lesions, all 49 melanomas from 49 patients were included in this study. Fifty randomly selected nonmelanomas from 46 patients served as a control. MAIN OUTCOME MEASURES: Ten dermoscopists independently examined dermoscopic images of 99 pigmented skin lesions and decided whether they identified the lesions as melanoma and whether they would recommend biopsy to rule out melanoma. Diagnostic and biopsy sensitivity and specificity were computed and then compared with the results of the computer-vision system. RESULTS: Dermoscopists were able to correctly identify small melanomas with an average diagnostic sensitivity of 39% and a specificity of 82% and recommended small melanomas for biopsy with a sensitivity of 71% and specificity of 49%, with only fair interobserver agreement (kappa = 0.31 for diagnosis and 0.34 for biopsy). In comparison, in recommending biopsy to rule out melanoma, the computer-vision system achieved 98% sensitivity and 44% specificity. CONCLUSIONS: Differentiation of small melanomas from small benign pigmented lesions challenges even expert physicians. Computer-vision systems can facilitate early detection of small melanomas and may limit the number of biopsies to rule out melanoma performed on benign lesions.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".