人工智能辅助光学活检改善了食管鳞状肿瘤的诊断。

Artificial intelligence-aided optical biopsy improves the diagnosis of esophageal squamous neoplasm.

作者信息

Ma Tian, Liu Guan-Qun, Guo Jing, Ji Rui, Shao Xue-Jun, Li Yan-Qing, Li Zhen, Zuo Xiu-Li

机构信息

Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan 250012, Shandong Province, China.

Qingdao Medicon Digital Engineering Company Limited, Qingdao 266000, Shandong Province, China.

出版信息

World J Gastroenterol. 2025 Apr 7;31(13):104370. doi: 10.3748/wjg.v31.i13.104370.

Abstract

BACKGROUND

Early detection of esophageal squamous neoplasms (ESN) is essential for improving patient prognosis. Optical diagnosis of ESN remains challenging. Probe-based confocal laser endomicroscopy (pCLE) enables accurate histological observation and optical biopsy of ESN. However, interpretation of pCLE images requires histopathological expertise and extensive training. Artificial intelligence (AI) has been widely applied in digestive endoscopy; however, AI for pCLE diagnosis of ESN has not been reported.

AIM

To develop a pCLE computer-aided diagnostic system for ESN and assess its diagnostic performance and assistant efficiency for nonexpert endoscopists.

METHODS

The intelligent confocal laser endomicroscopy (iCLE) system consists of image recognition (based on inception-ResNet V2), video diagnosis, and quality judgment modules. This system was developed using pCLE images and videos and evaluated through image and prospective video recognition tests. Patients between June 2020 and January 2023 were prospectively enrolled. Expert and non-expert endoscopists and the iCLE independently performed diagnoses for pCLE videos, with histopathology as the gold standard. Thereafter, the non-expert endoscopists performed a second assessment with iCLE assistance.

RESULTS

A total of 25056 images from 2803 patients were selected for iCLE training and validation. Another 2442 images from 226 patients were used for testing. iCLE achieved a high accuracy of 98.3%, sensitivity of 95.3% and specificity of 98.8% for diagnosing ESN images. A total of 2581 patients underwent upper gastrointestinal pCLE examination and were prospectively screened; 54 patients with suspected ESN were enrolled. Overall, 187 videos from 67 lesions were assessed by iCLE, three nonexpert and three expert endoscopists. iCLE achieved a high accuracy, sensitivity and specificity of 90.9%, 92.0%, and 90.2%, respectively. Compared to experts, iCLE showed significantly higher sensitivity (92.0% 80.4%; < 0.001) and negative predictive value (94.4% 87.7%; = 0.003). With iCLE assistance, nonexpert endoscopists showed significant improvements in accuracy (from 83.6% to 88.6%) and sensitivity (from 76.0% to 89.8%).

CONCLUSION

iCLE system demonstrated high diagnostic performance for ESN. It can assist nonexpert endoscopists in improving the diagnostic efficiency of pCLE for ESN and has the potential for reducing unnecessary biopsies.

摘要

背景

早期发现食管鳞状肿瘤(ESN)对于改善患者预后至关重要。ESN的光学诊断仍然具有挑战性。基于探头的共聚焦激光内镜检查(pCLE)能够对ESN进行准确的组织学观察和光学活检。然而,解读pCLE图像需要组织病理学专业知识和大量培训。人工智能(AI)已广泛应用于消化内镜检查;然而,尚未有关于AI用于pCLE诊断ESN的报道。

目的

开发一种用于ESN的pCLE计算机辅助诊断系统,并评估其对非专业内镜医师的诊断性能和辅助效率。

方法

智能共聚焦激光内镜检查(iCLE)系统由图像识别(基于Inception-ResNet V2)、视频诊断和质量判断模块组成。该系统使用pCLE图像和视频进行开发,并通过图像和前瞻性视频识别测试进行评估。前瞻性纳入2020年6月至2023年1月期间的患者。专家和非专家内镜医师以及iCLE独立对pCLE视频进行诊断,以组织病理学作为金标准。此后,非专家内镜医师在iCLE辅助下进行第二次评估。

结果

共选择2803例患者的25056张图像用于iCLE训练和验证。另外226例患者的2442张图像用于测试。iCLE在诊断ESN图像方面达到了98.3%的高准确率、95.3%的灵敏度和98.8%的特异性。共有2581例患者接受了上消化道pCLE检查并进行前瞻性筛查;54例疑似ESN患者被纳入。总体而言,iCLE、三名非专家和三名专家内镜医师对67个病变的187个视频进行了评估。iCLE分别达到了90.9%、92.0%和90.2%的高准确率、灵敏度和特异性。与专家相比,iCLE显示出显著更高的灵敏度(92.0%对80.4%;P<0.001)和阴性预测值(94.4%对87.7%;P=0.003)。在iCLE辅助下,非专家内镜医师在准确率(从83.6%提高到88.6%)和灵敏度(从76.0%提高到89.8%)方面有显著提高。

结论

iCLE系统在ESN诊断方面表现出较高的诊断性能。它可以帮助非专家内镜医师提高pCLE对ESN的诊断效率,并有可能减少不必要的活检。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3815/12001168/e818376688cc/104370-g001.jpg

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