Silver Frederick H, Mesica Arielle, Gonzalez-Mercedes Michael, Deshmukh Tanmay
Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, The State University of New Jersey, Piscataway, NJ 08854, USA.
OptoVibronex, LLC, Bethlehem, PA 18015, USA.
Cancers (Basel). 2022 Dec 27;15(1):156. doi: 10.3390/cancers15010156.
In this pilot study, we used vibrational optical tomography (VOCT), along with machine learning, to evaluate the specificity and sensitivity of using light and audible sound to differentiate between normal skin and skin cancers. The results reported indicate that the use of machine learning, and the height and location of the VOCT mechanovibrational peaks, have potential for being used to noninvasively differentiate between normal skin and different cancerous lesions. VOCT data, along with machine learning, is shown to predict the differences between normal skin and different skin cancers with a sensitivity and specificity at rates between 78 and 90%. The sensitivity and specificity will be improved using a larger database and by using other AI techniques. Ultimately, VOCT data, visual inspection, and dermoscopy, in conjunction with machine learning, will be useful in telemedicine to noninvasively identify potentially malignant skin cancers in remote areas of the country where dermatologists are not readily available.
在这项初步研究中，我们使用振动光学断层扫描（VOCT）并结合机器学习，来评估利用光和可听声音区分正常皮肤与皮肤癌的特异性和敏感性。报告的结果表明，机器学习的应用以及VOCT机械振动峰的高度和位置，具有用于无创区分正常皮肤与不同癌性病变的潜力。VOCT数据与机器学习相结合，被证明能够以78%至90%的灵敏度和特异性预测正常皮肤与不同皮肤癌之间的差异。使用更大的数据库并采用其他人工智能技术将提高灵敏度和特异性。最终，VOCT数据、目视检查和皮肤镜检查，再结合机器学习，将有助于远程医疗在该国皮肤科医生不易到达的偏远地区无创识别潜在的恶性皮肤癌。
