PDF(Pubmed)
Abstract：
Background: This study aimed to evaluate the potential of human-machine interaction (HMI) in a deep learning software for discerning the malignancy of choroidal melanocytic lesions based on fundus photographs. Methods: The study enrolled individuals diagnosed with a choroidal melanocytic lesion at a tertiary clinic between 2011 and 2023, resulting in a cohort of 762 eligible cases. A deep learning-based assistant integrated into the software underwent training using a dataset comprising 762 color fundus photographs (CFPs) of choroidal lesions captured by various fundus cameras. The dataset was categorized into benign nevi, untreated choroidal melanomas, and irradiated choroidal melanomas. The reference standard for evaluation was established by retinal specialists using multimodal imaging. Trinary and binary models were trained, and their classification performance was evaluated on a test set consisting of 100 independent images. The discriminative performance of deep learning models was evaluated based on accuracy, recall, and specificity. Results: The final accuracy rates on the independent test set for multi-class and binary (benign vs. malignant) classification were 84.8% and 90.9%, respectively. Recall and specificity ranged from 0.85 to 0.90 and 0.91 to 0.92, respectively. The mean area under the curve (AUC) values were 0.96 and 0.99, respectively. Optimal discriminative performance was observed in binary classification with the incorporation of a single imaging modality, achieving an accuracy of 95.8%. Conclusions: The deep learning models demonstrated commendable performance in distinguishing the malignancy of choroidal lesions. The software exhibits promise for resource-efficient and cost-effective pre-stratification.
摘要：
背景：本研究旨在评估深度学习软件中人机交互（HMI）的潜力，以根据眼底照片识别脉络膜黑色素细胞病变的恶性程度。方法：该研究招募了2011年至2023年在三级诊所诊断为脉络膜黑素细胞病变的个体，结果是762例合格病例。集成到软件中的基于深度学习的助手使用包含由各种眼底相机捕获的脉络膜病变的762张彩色眼底照片（CFP）的数据集进行训练。数据集被归类为良性痣，未经治疗的脉络膜黑色素瘤，和放射性脉络膜黑素瘤。视网膜专家使用多模态成像建立了评估的参考标准。训练了三进制和二元模型，在由100张独立图像组成的测试集上评估了它们的分类性能。基于准确性评估了深度学习模型的判别性能，召回，和特异性。结果：多类和二元的独立测试集上的最终准确率(良性与恶性）分类分别为84.8%和90.9%，分别。召回率和特异性分别为0.85至0.90和0.91至0.92。曲线下平均面积(AUC)值分别为0.96和0.99。在结合了单个成像模态的二元分类中观察到了最佳的判别性能，达到95.8%的准确率。结论：深度学习模型在区分脉络膜病变的恶性方面表现出良好的性能。该软件有望实现资源高效和成本有效的预分层。
