PDF(Pubmed)
Abstract：
This study presents an innovative application of artificial intelligence (AI) in distinguishing dermoscopy images depicting individuals with benign and malignant skin lesions. Leveraging the collaborative capabilities of Google\'s platform, the developed model exhibits remarkable efficiency in achieving accurate diagnoses. The model underwent training for a mere one hour and 33 minutes, utilizing Google\'s servers to render the process both cost-free and carbon-neutral. Utilizing a dataset representative of both benign and malignant cases, the AI model demonstrated commendable performance metrics. Notably, the model achieved an overall accuracy, precision, recall (sensitivity), specificity, and F1 score of 92%. These metrics underscore the model\'s proficiency in distinguishing between benign and malignant skin lesions. The use of Google\'s Collaboration platform not only expedited the training process but also exemplified a cost-effective and environmentally sustainable approach. While these findings highlight the potential of AI in dermatopathology, it is crucial to recognize the inherent limitations, including dataset representativity and variations in real-world clinical scenarios. This study contributes to the evolving landscape of AI applications in dermatologic diagnostics, showcasing a promising tool for accurate lesion classification. Further research and validation studies are recommended to enhance the model\'s robustness and facilitate its integration into clinical practice.
摘要：
这项研究提出了人工智能（AI）在区分皮肤镜检查图像方面的创新应用，这些图像描绘了患有良性和恶性皮肤病变的个体。利用谷歌平台的协作能力，所开发的模型在实现准确诊断方面表现出显著的效率。该模型只接受了1小时33分钟的训练，利用谷歌的服务器，使过程既无成本又碳中和。利用代表良性和恶性病例的数据集，人工智能模型展示了值得称赞的性能指标。值得注意的是,该模型实现了整体准确性，精度,召回(敏感度)，特异性，F1得分为92％。这些指标强调了模型在区分良性和恶性皮肤病变方面的熟练程度。Google协作平台的使用不仅加快了培训过程，而且体现了一种具有成本效益和环境可持续性的方法。虽然这些发现突出了AI在皮肤病理学中的潜力，认识到固有的局限性至关重要，包括数据集代表性和真实世界临床场景中的变化。这项研究有助于AI在皮肤科诊断中的应用不断发展，展示了一个有前途的工具，用于准确的病变分类。建议进一步研究和验证研究，以增强模型的鲁棒性，并促进其融入临床实践。
