Background: Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model. Aim: To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images. Methods: A monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors. Results: We reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task. Conclusion: Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight.
背景：尽管宫腔镜联合子宫内膜活检是诊断子宫内膜病变的金标准，但妇科医生的经验对于正确诊断至关重要。深度学习作为人工智能方法，可能有助于克服这一局限性。然而目前仅有初步研究结果，尚缺乏评估深度学习模型在识别宫内病变方面性能的研究，也未能探讨在模型中纳入临床因素可能带来的辅助价值。 目的：开发一种深度学习模型，作为从宫腔镜图像中自动检测和分类子宫内膜病变的工具。 方法：本研究为单中心观察性回顾性队列研究，通过查阅2021年1月至2021年5月期间在本中心经病理确诊宫内病变的连续患者的临床记录、电子数据库及存储的宫腔镜检查视频资料。提取的宫腔镜图像用于构建深度学习模型，在有无临床因素辅助的情况下对宫腔内病变进行分类识别。研究主要结局指标为深度学习模型在有无临床因素辅助下对宫腔内病变进行分类识别的诊断效能指标。 结果：我们分析了来自266例患者的1500张图像：其中186例为良性局灶性病变，25例为良性弥漫性病变，55例为癌前/癌性病变。在分类和识别任务中，结合临床因素辅助均获得最佳性能：分类任务总体精确率为80.11%，召回率为80.11%，特异性为90.06%，F1分数为80.11%，准确率为86.74%；识别任务总体检出率为85.82%，精确率为93.12%，召回率为91.63%，F1分数为92.37%。 结论：本研究开发的深度学习模型在宫腔镜图像中对宫腔内病变的检测和分类诊断效能较低。虽然结合临床数据可获得最佳诊断性能，但这种改善幅度有限。
Detection and Classification of Hysteroscopic Images Using Deep Learning
肿瘤(癌症)患者之家