卷积神经网络从B超图像中分类肝脂肪变性的诊断准确性：一项在印度特伦甘纳邦社区环境中进行的系统评价、荟萃分析及新验证研究
Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India.
作者信息
Jagadeesh Akshay, Aramrat Chanchanok, Rai Santosh, Maqsood Fathima Hana, Madhukeshwar Adarsh Kibballi, Bhogadi Santhi, Lieber Judith, Mahajan Hemant, Banjara Santosh Kumar, Lewin Alexandra, Kinra Sanjay, Mallinson Poppy
机构信息
Department of Non-communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK.
Department of Radiology, Kasturba Medical College Mangalore, Manipal Academy of Higher Education, Karnataka, 576 104, India.
出版信息
Lancet Reg Health Southeast Asia. 2025 Jul 31;40:100644. doi: 10.1016/j.lansea.2025.100644. eCollection 2025 Sep.
BACKGROUND
Ultrasound is a widely available, inexpensive, and non-invasive modality for evaluating hepatic steatosis (HS). However, the scarcity of radiological expertise limits its utility. Convolutional Neural Networks (CNNs) have potential for automated classification of HS using B-mode ultrasound images. We aimed to assess their diagnostic accuracy and generalisability across diverse study settings and populations.
METHODS
We systematically reviewed two biomedical databases up to Dec 12, 2023, to identify studies that applied CNNs in the classification of HS using B-mode ultrasound images as input (PROSPERO: CRD42024501483). We supplemented this review with a novel analysis of the community-based Andhra Pradesh Children and Parents' Study (APCAPS) in India to address the overrepresentation of hospital samples and lack of data on South Asian populations who exhibit a distinct central adiposity phenotype that could influence CNN performance. We quantitatively synthesised diagnostic accuracy metrics for eligible studies using random-effects meta-analyses.
FINDINGS
Our search returned 289 studies, of which 17 were eligible. All but one of the 17 studies were based in hospital or clinical outpatient settings with curated cases and controls. Studies were conducted exclusively in East Asian, European, or North American populations. Studies employed varying gold standards: seven studies (41.18%) used liver biopsy, three (17.64%) used MRI proton density fat fraction, and seven (41.18%) used clinician-evaluated ultrasound-based HS grades. The APCAPS sample included 219 participants with radiologist-assigned HS grades. Across the range of study settings and populations, CNNs demonstrated good diagnostic accuracy. Meta-analysis of studies with low risk of bias reporting on five unique datasets showed a pooled area under the receiver operating characteristic curve of 0.93 (95% CI 0.73-0.98) for detecting any severity and 0.86 (95% CI 0.77-0.92) for detecting moderate-to-severe HS severity grades, respectively.
INTERPRETATION
CNNs have good diagnostic accuracy and generalisability for HS classification, suggesting potential for real-world application.
FUNDING
Medical Research Council, UK (MR/T038292/1, MR/V001221/1).
背景
超声是一种广泛可用、价格低廉且无创的评估肝脂肪变性（HS）的方式。然而，放射学专业知识的稀缺限制了其效用。卷积神经网络（CNN）有潜力利用B超图像对HS进行自动分类。我们旨在评估其在不同研究环境和人群中的诊断准确性及通用性。
方法
我们系统检索了截至2023年12月12日的两个生物医学数据库，以识别将CNN应用于以B超图像为输入的HS分类研究（国际前瞻性系统评价注册库：CRD42024501483）。我们通过对印度基于社区的安得拉邦儿童与父母研究（APCAPS）进行新颖分析来补充本综述，以解决医院样本占比过高以及缺乏关于南亚人群数据的问题，南亚人群具有独特的中心性肥胖表型，可能会影响CNN性能。我们使用随机效应荟萃分析对符合条件的研究的诊断准确性指标进行定量综合分析。
结果
我们的检索返回了289项研究，其中17项符合条件。这17项研究中除一项外均基于医院或临床门诊环境，有经过挑选的病例和对照。研究仅在东亚、欧洲或北美人群中开展。研究采用了不同的金标准：7项研究（41.18%）使用肝活检，3项（17.64%）使用MRI质子密度脂肪分数，7项（41.18%）使用临床医生评估的基于超声的HS分级。APCAPS样本包括219名有放射科医生指定HS分级的参与者。在一系列研究环境和人群中，CNN显示出良好的诊断准确性。对五个独特数据集进行低偏倚风险报告的研究的荟萃分析显示，检测任何严重程度时，受试者工作特征曲线下的合并面积为0.93（95%CI 0.73 - 0.98），检测中度至重度HS严重程度分级时为0.86（95%CI 0.77 - 0.92）。
解读
CNN对HS分类具有良好的诊断准确性和通用性，表明其在实际应用中有潜力。
资助
英国医学研究理事会（MR/T038292/1，MR/V001221/1）。
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