Tungki Pratama Umar, Nityanand Jain, Manthia Papageorgakopoulou, Rahma Sameh Shaheen, Jehad Feras Alsamhori, Muhammad Muzzamil, Andrejs Kostiks
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The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools.</p><p><strong>Results: </strong>In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). Based on type of input for classification, the pooled sensitivity of AI models for gait, electromyography, and magnetic resonance signals was 91.2%, 92.6%, and 82.2%, respectively. The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively.</p><p><strong>Conclusions: </strong>Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.</p>","PeriodicalId":72184,"journal":{"name":"Amyotrophic lateral sclerosis & frontotemporal degeneration","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis.\",\"authors\":\"Tungki Pratama Umar, Nityanand Jain, Manthia Papageorgakopoulou, Rahma Sameh Shaheen, Jehad Feras Alsamhori, Muhammad Muzzamil, Andrejs Kostiks\",\"doi\":\"10.1080/21678421.2024.2334836\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS is challenging due to the absence of a specific detection test. The use of artificial intelligence (AI) can assist in the investigation and treatment of ALS.</p><p><strong>Methods: </strong>We searched seven databases for literature on the application of AI in the early diagnosis and screening of ALS in humans. The findings were summarized using random-effects summary receiver operating characteristic curve. The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools.</p><p><strong>Results: </strong>In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). 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The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively.</p><p><strong>Conclusions: </strong>Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.</p>\",\"PeriodicalId\":72184,\"journal\":{\"name\":\"Amyotrophic lateral sclerosis & frontotemporal degeneration\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Amyotrophic lateral sclerosis & frontotemporal degeneration\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21678421.2024.2334836\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/4/2 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Amyotrophic lateral sclerosis & frontotemporal degeneration","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21678421.2024.2334836","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/4/2 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
简介肌萎缩性脊髓侧索硬化症(ALS)是一种罕见的致命性神经系统疾病,会导致进行性运动功能退化。由于缺乏特异性检测试验,诊断 ALS 具有挑战性。使用人工智能(AI)可以帮助调查和治疗 ALS:我们在七个数据库中搜索了有关人工智能在人类 ALS 早期诊断和筛查中应用的文献。研究结果采用随机效应总结接收器操作特征曲线进行总结。使用QUADAS-2或QUADAS-C工具进行偏倚风险(RoB)分析:在 34 项分析研究中,ALS 的元患病率为 47%。在 ALS 检测方面,人工智能模型的集合灵敏度为 94.3%(95% CI - 63.2% 至 99.4%),集合特异性为 98.9%(95% CI - 92.4% 至 99.9%)。对于 ALS 分类,人工智能模型的集合灵敏度为 90.9%(95% CI - 86.5% 至 93.9%),集合特异性为 92.3%(95% CI - 84.8% 至 96.3%)。根据分类输入的类型,步态、肌电图和磁共振信号的人工智能模型的集合灵敏度分别为 91.2%、92.6% 和 82.2%。步态、肌电图和磁共振信号的集合特异性分别为 94.1%、96.5% 和 77.3%:尽管人工智能因其高灵敏度和高特异性可在 ALS 的筛查和诊断中发挥重要作用,但文献报道的证据质量仍令人担忧。
Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis.
Introduction: Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS is challenging due to the absence of a specific detection test. The use of artificial intelligence (AI) can assist in the investigation and treatment of ALS.
Methods: We searched seven databases for literature on the application of AI in the early diagnosis and screening of ALS in humans. The findings were summarized using random-effects summary receiver operating characteristic curve. The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools.
Results: In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). Based on type of input for classification, the pooled sensitivity of AI models for gait, electromyography, and magnetic resonance signals was 91.2%, 92.6%, and 82.2%, respectively. The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively.
Conclusions: Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.