The Diagnostic Accuracy of in vivo Confocal Scanning Laser Microscopy Compared to Dermoscopy of Benign and Malignant Melanocytic Lesions: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: The diagnosis of melanoma at an early, curable stage is an important challenge for clinicians. Confocal scanning laser microscopy (CSLM) is a high-resolution, noninvasive technology that may facilitate improved diagnostic accuracy over clinical examination. The aim of this study was to evaluate the diagnostic accuracy of CSLM compared to dermoscopy in a prospective examination of benign and malignant melanocytic lesions. METHODS: 125 patients with suspicious pigmented lesions were prospectively recruited to undergo a clinical, dermoscopic and CSLM examination. A diagnosis was made preoperatively with each technique, and the lesion was then excised and diagnosed using histopathology. RESULTS: 125 patients with 125 lesions were studied comprising 88 melanocytic nevi and 37 melanomas. Dermoscopy had a sensitivity of 89.2%, a specificity of 84.1%, a positive predictive value of 70.2% and a negative predictive value of 94.9%. CSLM was found to have a sensitivity of 97.3%, a specificity of 83.0%, a positive predictive value of 70.6% and a negative predictive value of 98.6%. No melanomas were misidentified when both techniques were used together. CONCLUSIONS: CSLM had a relatively higher sensitivity than dermoscopy; however, the specificity was similar with CSLM and dermoscopy. These results suggest that dermoscopy and CSLM are complementary.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| 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".