人工智能在非酒精性脂肪性肝病和肝纤维化中的应用：一项系统评价和荟萃分析。
Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis.
作者信息
Decharatanachart Pakanat, Chaiteerakij Roongruedee, Tiyarattanachai Thodsawit, Treeprasertsuk Sombat
机构信息
Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama IV Rd., Pathum Wan, Bangkok 10330, ThailandCenter of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
出版信息
Therap Adv Gastroenterol. 2021 Dec 21;14:17562848211062807. doi: 10.1177/17562848211062807. eCollection 2021.
BACKGROUND
The global prevalence of non-alcoholic fatty liver disease (NAFLD) continues to rise. Non-invasive diagnostic modalities including ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy but with limited performance. Artificial intelligence (AI) is currently being integrated with conventional diagnostic methods in the hopes of performance improvements. We aimed to estimate the performance of AI-assisted systems for diagnosing NAFLD, non-alcoholic steatohepatitis (NASH), and liver fibrosis.
METHODS
A systematic review was performed to identify studies integrating AI in the diagnosis of NAFLD, NASH, and liver fibrosis. Pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and summary receiver operating characteristic curves were calculated.
RESULTS
Twenty-five studies were included in the systematic review. Meta-analysis of 13 studies showed that AI significantly improved the diagnosis of NAFLD, NASH and liver fibrosis. AI-assisted ultrasonography had excellent performance for diagnosing NAFLD, with a sensitivity, specificity, PPV, NPV of 0.97 (95% confidence interval (CI): 0.91-0.99), 0.98 (95% CI: 0.89-1.00), 0.98 (95% CI: 0.93-1.00), and 0.95 (95% CI: 0.88-0.98), respectively. The performance of AI-assisted ultrasonography was better than AI-assisted clinical data sets for the identification of NAFLD, which provided a sensitivity, specificity, PPV, NPV of 0.75 (95% CI: 0.66-0.82), 0.82 (95% CI: 0.74-0.88), 0.75 (95% CI: 0.60-0.86), and 0.82 (0.74-0.87), respectively. The area under the curves were 0.98 and 0.85 for AI-assisted ultrasonography and AI-assisted clinical data sets, respectively. AI-integrated clinical data sets had a pooled sensitivity, specificity of 0.80 (95%CI: 0.75-0.85), 0.69 (95%CI: 0.53-0.82) for identifying NASH, as well as 0.99-1.00 and 0.76-1.00 for diagnosing liver fibrosis stage F1-F4, respectively.
CONCLUSION
AI-supported systems provide promising performance improvements for diagnosing NAFLD, NASH, and identifying liver fibrosis among NAFLD patients. Prospective trials with direct comparisons between AI-assisted modalities and conventional methods are warranted before real-world implementation.
PROTOCOL REGISTRATION
PROSPERO (CRD42021230391).
背景
非酒精性脂肪性肝病（NAFLD）的全球患病率持续上升。包括超声检查和临床评分系统在内的非侵入性诊断方法已被提议作为肝活检的替代方法，但性能有限。目前人工智能（AI）正与传统诊断方法相结合，以期提高性能。我们旨在评估人工智能辅助系统诊断NAFLD、非酒精性脂肪性肝炎（NASH）和肝纤维化的性能。
方法
进行系统评价以确定将人工智能整合到NAFLD、NASH和肝纤维化诊断中的研究。计算合并敏感度、特异度、阳性预测值（PPV）、阴性预测值（NPV）以及汇总的受试者工作特征曲线。
结果
系统评价纳入了25项研究。对13项研究的荟萃分析表明，人工智能显著改善了NAFLD、NASH和肝纤维化的诊断。人工智能辅助超声检查在诊断NAFLD方面具有出色的性能，敏感度、特异度、PPV、NPV分别为0.97（95%置信区间（CI）：0.91 - 0.99）、0.98（95%CI：0.89 - 1.00）、0.98（95%CI：0.93 - 1.00）和0.95（95%CI：0.88 - 0.98）。在识别NAFLD方面，人工智能辅助超声检查的性能优于人工智能辅助临床数据集，后者提供的敏感度、特异度、PPV、NPV分别为0.75（95%CI：0.66 - 0.82）、0.82（95%CI：0.74 - 0.88）、0.75（95%CI：0.60 - 0.86）和0.82（0.74 - 0.87）。人工智能辅助超声检查和人工智能辅助临床数据集的曲线下面积分别为0.98和0.85。整合了人工智能的临床数据集在识别NASH方面的合并敏感度、特异度分别为0.80（95%CI：0.75 - 0.85）、0.69（95%CI：0.53 - 0.82），在诊断肝纤维化F1 - F4期方面分别为0.99 - 1.00和0.76 - 1.00。
结论
人工智能支持的系统在诊断NAFLD、NASH以及识别NAFLD患者的肝纤维化方面提供了有前景的性能提升。在实际应用之前，有必要进行人工智能辅助模式与传统方法直接比较的前瞻性试验。
方案注册
PROSPERO（CRD42021230391）
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