Artificial Intelligence to Detect Developmental Dysplasia of Hip: A Systematic Review.

IF 1.4 4区 医学 Q2 PEDIATRICS
Suketu Bhavsar, Bhanu B Gowda, Maulini Bhavsar, Sanjay Patole, Shripada Rao, Chandra Rath
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Abstract

Aim: Deep learning (DL), a branch of artificial intelligence (AI), has been applied to diagnose developmental dysplasia of the hip (DDH) on pelvic radiographs and ultrasound (US) images. This technology can potentially assist in early screening, enable timely intervention and improve cost-effectiveness. We conducted a systematic review to evaluate the diagnostic accuracy of the DL algorithm in detecting DDH.

Methods: PubMed, Medline, EMBASE, EMCARE, the clinicaltrials.gov (clinical trial registry), IEEE Xplore and Cochrane Library databases were searched in October 2024. Prospective and retrospective cohort studies that included children (< 16 years) at risk of or suspected to have DDH and reported hip ultrasonography (US) or X-ray images using AI were included. A review was conducted using the guidelines of the Cochrane Collaboration Diagnostic Test Accuracy Working Group. Risk of bias was assessed using the QUADAS-2 tool.

Results: Twenty-three studies met inclusion criteria, with 15 (n = 8315) evaluating DDH on US images and eight (n = 7091) on pelvic radiographs. The area under the curve of the included studies ranged from 0.80 to 0.99 for pelvic radiographs and 0.90-0.99 for US images. Sensitivity and specificity for detecting DDH on radiographs ranged from 92.86% to 100% and 95.65% to 99.82%, respectively. For US images, sensitivity ranged from 86.54% to 100% and specificity from 62.5% to 100%.

Conclusion: AI demonstrated comparable effectiveness to physicians in detecting DDH. However, limited evaluation on external datasets restricts its generalisability. Further research incorporating diverse datasets and real-world applications is needed to assess its broader clinical impact on DDH diagnosis.

人工智能检测髋关节发育不良:系统综述。
目的:深度学习(DL)是人工智能(AI)的一个分支,已被应用于盆腔x线片和超声(US)图像上诊断髋关节发育不良(DDH)。这项技术可能有助于早期筛查,实现及时干预并提高成本效益。我们进行了一项系统综述,以评估DL算法在检测DDH时的诊断准确性。方法:检索2024年10月的PubMed、Medline、EMBASE、EMCARE、clinicaltrials.gov(临床试验注册)、IEEE Xplore和Cochrane Library数据库。包括儿童在内的前瞻性和回顾性队列研究(结果:23项研究符合纳入标准,其中15项(n = 8315)评估US图像上的DDH, 8项(n = 7091)评估骨盆x线片上的DDH。在纳入的研究中,骨盆x线片的曲线下面积为0.80 -0.99,超声图像的曲线下面积为0.90-0.99。x线片检测DDH的灵敏度为92.86% ~ 100%,特异性为95.65% ~ 99.82%。对于US图像,灵敏度为86.54% ~ 100%,特异性为62.5% ~ 100%。结论:人工智能在检测DDH方面表现出与医生相当的有效性。然而,对外部数据集的有限评估限制了其通用性。需要进一步的研究结合不同的数据集和实际应用来评估其对DDH诊断的更广泛的临床影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.90
自引率
5.90%
发文量
487
审稿时长
3-6 weeks
期刊介绍: The Journal of Paediatrics and Child Health publishes original research articles of scientific excellence in paediatrics and child health. Research Articles, Case Reports and Letters to the Editor are published, together with invited Reviews, Annotations, Editorial Comments and manuscripts of educational interest.
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