Raimondo Diego, Raffone Antonio, Salucci Paolo, Raimondo Ivano, Capobianco Giampiero, Galatolo Federico Andrea, Cimino Mario Giovanni Cosimo Antonio, Travaglino Antonio, Maletta Manuela, Ferla Stefano, Virgilio Agnese, Neola Daniele, Casadio Paolo, Seracchioli Renato
Division of Gynaecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy.
Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy.
Cancers (Basel). 2024 Mar 28;16(7):1315. doi: 10.3390/cancers16071315.
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.
To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images.
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.
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.
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.
尽管宫腔镜检查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（DL）作为一种人工智能方法，可能有助于克服这一局限性。不幸的是，目前仅有初步研究结果，尚无评估DL模型在识别宫腔内病变方面的性能以及模型纳入临床因素后可能提供的帮助的研究。
开发一种DL模型，作为从宫腔镜图像中检测和分类子宫内膜病变的自动化工具。
通过回顾2021年1月至2021年5月在我们中心连续的经病理证实有宫腔内病变患者的临床记录、电子数据库和存储的宫腔镜视频，进行了一项单中心观察性回顾性队列研究。检索到的宫腔镜图像用于构建一个DL模型，用于在有或没有临床因素帮助的情况下对宫腔内子宫病变进行分类和识别。研究结果是DL模型在有和没有临床因素帮助的情况下对宫腔内子宫病变进行分类和识别的诊断指标。
我们回顾了266例患者的1500张图像：186例患者有良性局灶性病变，25例有良性弥漫性病变，55例有癌前/肿瘤性病变。对于分类和识别任务，在临床因素的帮助下取得了最佳性能，分类任务的总体精度为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%；识别任务的总体检测率为85.82%，精度为93.12%，召回率为91.63%，F1分数为92.37%。
我们的DL模型在从宫腔镜图像中检测和分类宫腔内子宫病变方面的诊断性能较低。尽管在临床数据的帮助下获得了最佳诊断性能，但这种改善很轻微。
