Clinical Validation of the Melanoscope AI Mobile Dermoscopy Clinical Decision Support System
Quick Take
The Melanoscope AI CDSS demonstrated 88.6% agreement with expert assessments and no false negatives in a clinical validation involving 176 patients, supporting its use for melanoma screening in resource-limited settings.
Key Points
- Achieved 88.6% agreement with expert assessments in a single-centre study.
- No false negatives found among 5 malignant lesions, indicating high reliability.
- Specificity of the system was recorded at 88.3%, supporting its screening capabilities.
- Utilized a cascade classification model with attention map visualization for interpretability.
- The study was conducted across four 'Melanoma Day' sessions in Orel, Russia.
Article Content
From source RSS / original summaryarXiv:2605. 27561v1 Announce Type: new Abstract: Introduction. Early detection of malignant skin lesions is critical for prognosis, yet dermatologist shortages in Russian regions limit screening coverage. Mobile dermoscopy clinical decision support systems (CDSS) offer a promising approach, with model interpretability and standardised patient routing remaining key barriers to adoption. Aim.
To develop a quantitative interpretability assessment method for cascade deep learning models and a three-zone patient routing algorithm, and to conduct a preliminary single-centre prospective clinical validation of the Melanoscope AI CDSS in Russian outpatient practice. Material and methods.
Two-stage cascade classification of dermoscopic images; attention map visualisation (attention rollout for ViT and Swin; Grad-CAM for ConvNeXt and EfficientNetV2); quantitative IoU-based agreement assessment between activation maps and expert annotations; prospective single-centre validation across four "Melanoma Day" sessions (Orel, Russia, June 2025 - April 2026). Results. On 176 patients: agreement with expert assessment 88. 6%; no false negatives among 5 malignant lesions (95% CI: 47. 8-100.
0%); specificity 88. 3%. Three melanomas and two basal cell carcinomas were histologically confirmed; six dysplastic naevi placed under follow-up. Mean IoU (n=180): ViT - 0. 69; Swin - 0. 64; ConvNeXt - 0. 53; EfficientNetV2 - 0. 51. Routing thresholds: P=0. 50. Conclusion. No false negatives were observed; specificity was 88. 3%, supporting screening use.
The integrated cascade classification, attention map visualisation with IoU assessment, and three-zone routing provide reproducible, interpretable clinical decision support adaptable to varying resource levels.
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