Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, P.R. China.
Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, P.R. China.
Clin Transl Gastroenterol. 2023 Oct 1;14(10):e00606. doi: 10.14309/ctg.0000000000000606.
Endoscopic evaluation is crucial for predicting the invasion depth of esophagus squamous cell carcinoma (ESCC) and selecting appropriate treatment strategies. Our study aimed to develop and validate an interpretable artificial intelligence-based invasion depth prediction system (AI-IDPS) for ESCC.
We reviewed the PubMed for eligible studies and collected potential visual feature indices associated with invasion depth. Multicenter data comprising 5,119 narrow-band imaging magnifying endoscopy images from 581 patients with ESCC were collected from 4 hospitals between April 2016 and November 2021. Thirteen models for feature extraction and 1 model for feature fitting were developed for AI-IDPS. The efficiency of AI-IDPS was evaluated on 196 images and 33 consecutively collected videos and compared with a pure deep learning model and performance of endoscopists. A crossover study and a questionnaire survey were conducted to investigate the system's impact on endoscopists' understanding of the AI predictions.
AI-IDPS demonstrated the sensitivity, specificity, and accuracy of 85.7%, 86.3%, and 86.2% in image validation and 87.5%, 84%, and 84.9% in consecutively collected videos, respectively, for differentiating SM2-3 lesions. The pure deep learning model showed significantly lower sensitivity, specificity, and accuracy (83.7%, 52.1% and 60.0%, respectively). The endoscopists had significantly improved accuracy (from 79.7% to 84.9% on average, P = 0.03) and comparable sensitivity (from 37.5% to 55.4% on average, P = 0.27) and specificity (from 93.1% to 94.3% on average, P = 0.75) after AI-IDPS assistance.
Based on domain knowledge, we developed an interpretable system for predicting ESCC invasion depth. The anthropopathic approach demonstrates the potential to outperform deep learning architecture in practice.
内镜评估对于预测食管鳞状细胞癌（ESCC）的浸润深度和选择合适的治疗策略至关重要。我们的研究旨在开发和验证一种基于可解释人工智能的 ESCC 浸润深度预测系统（AI-IDPS）。
我们在 PubMed 上检索了合格的研究，并收集了与浸润深度相关的潜在视觉特征指数。从 2016 年 4 月至 2021 年 11 月，我们从 4 家医院收集了包含 581 名 ESCC 患者的 5119 例窄带成像放大内镜图像的多中心数据。为 AI-IDPS 开发了 13 种特征提取模型和 1 种特征拟合模型。我们在 196 张图像和 33 个连续采集的视频上评估了 AI-IDPS 的效率，并将其与纯深度学习模型和内镜医生的表现进行了比较。进行了一项交叉研究和问卷调查，以调查该系统对内镜医生理解 AI 预测的影响。
AI-IDPS 在图像验证中分别显示出 85.7%、86.3%和 86.2%的敏感性、特异性和准确性，在连续采集的视频中分别显示出 87.5%、84%和 84.9%的敏感性、特异性和准确性，用于区分 SM2-3 病变。纯深度学习模型的敏感性、特异性和准确性显著较低（分别为 83.7%、52.1%和 60.0%）。AI-IDPS 辅助后，内镜医生的准确性有显著提高（平均从 79.7%提高到 84.9%，P=0.03），敏感性（平均从 37.5%提高到 55.4%，P=0.27）和特异性（平均从 93.1%提高到 94.3%，P=0.75）相当。
基于领域知识，我们开发了一种用于预测 ESCC 浸润深度的可解释系统。基于人工的方法在实践中表现出优于深度学习架构的潜力。
