Supplementary Material for: In vivo study to evaluate an intelligent algorithm for time efficient detection of malignant melanoma using dermatofluoroscopy

Introduction: Dermatofluoroscopy is an optical non-invasive method of melanoma/nevus differentiation that has shown 89% sensitivity and 45% specificity in clinical trials, but long measurement duration hinders clinical use. An intelligent algorithm was developed to
shorten the measurement time without compromising its diagnostic accuracy. It uses dermoscopic images of the skin lesions to be measured to select measurement points based on the assessment of color values.
Methods: 27 patients with a total of 29 lesions suggestive of cutaneous melanoma were included in a clinical study and measured with both methods, conventional dermatofluoroscopy and the newly developed intelligent algorithm. The results were compared to the independent findings of two histopathologists to evaluate diagnostic accuracy and time saved.
Results: There was a median reduction of measurement points from 265 to 158 (40%). Meanwhile, the intelligent algorithm showed a higher diagnostic accuracy than conventional dermatofluoroscopy (AUC of 72% vs. 63%).
Conclusion: The intelligent algorithm did not perform inferior to the conventional method while saving 40% of time. However, measurement times remain long compared to other non-invasive methods of diagnosing malignant melanoma. Further studies are needed to evaluate clinical suitability.

引言：皮肤荧光镜检技术是一种光学非侵入性手段，用于区分黑色素瘤/痣，其在临床试验中展现出89%的敏感性及45%的特异性，然而，测量时间的长久性限制了其在临床上的应用。为此，一种智能算法得以开发，旨在缩短测量时间，同时不牺牲其诊断精度。该算法通过分析待测皮肤病变的皮肤镜图像，根据颜色值的评估选择测量点。方法：27名患者共29个疑似皮肤黑色素瘤的病变被纳入一项临床研究，并使用传统皮肤荧光镜检与新型智能算法两种方法进行测量。研究结果与两位病理学家的独立发现进行比较，以评估诊断精度及节省的时间。结果：测量点的中位数从265减少到158（减少了40%）。同时，智能算法在诊断精度上优于传统皮肤荧光镜检（AUC为72% vs. 63%）。结论：智能算法在节省40%时间的同时，并未在诊断性能上劣于传统方法。然而，与诊断恶性黑色素瘤的其他非侵入性方法相比，测量时间仍然较长。为进一步评估其临床适用性，需要进一步的研究。