Deep Tissue Sequencing Using Hypodermoscopy and Augmented Intelligence to Analyze Atypical Pigmented Lesions
Bibliographic record
Abstract
BACKGROUND: Over the past decade, new technologies, devices, and methods have been developed to assist in the diagnosis of cutaneous melanocytic lesions. OBJECTIVE: Our objective was to evaluate the performance of an augmented intelligence system in the assessment of atypical pigmented lesions. METHODS: Nine atypical pigmented lesions on 8 patients were evaluated prior to surgical removal. No lesions had received previous treatment other than a diagnostic biopsy. Prior to surgical removal, each lesion was evaluated by an Augmented Intelligence Dermal Imager (AID) and the assessment parameters reviewed in light of the final histopathological diagnosis. RESULTS: The AID was used to evaluate a limited set of atypical pigmented lesions and showed sensitivity and specificity of 82% and 61%, respectively, based on its internal risk assessment algorithms. LIMITATIONS: These cases represent early assessments of the AID in a clinical setting, all prior assessments having been carried out on digital images. The information received from these evaluations requires further validation and analysis to be able to extrapolate its clinical usefulness. CONCLUSION: The AID combines dermoscopy, hypodermoscopy, and a trained augmented algorithm to produce a diffusion map representing the features of each lesion compared to the learned characteristics from a database of known dermoscopy images of lesions with definitive prior diagnosis. The information gathered from the diffusion map might be used to calculate a malignancy risk factor for the lesion compared to known melanoma features. This malignancy risk factor could be helpful in providing information to justify the biopsy of an atypical pigmented lesion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".