Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis

Tungki Pratama Umar, Nityanand Jain (Corresponding Author), Manthia Papageorgakopoulou, Rahma Sameh Shaheen, Jehad Feras AlSamhori, Muhammad Muzzamil, Andrejs Kostiks

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)425-436
Number of pages12
JournalAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration
Volume25
Issue number5-6
DOIs
Publication statusPublished - 2024

Keywords*

  • amyotrophic lateral sclerosis
  • ALS
  • artificial intelligence
  • AI
  • diagnostiics
  • sensitivity
  • specificity

Field of Science*

  • 1.2 Computer and information sciences
  • 3.2 Clinical medicine

Publication Type*

  • 1.1. Scientific article indexed in Web of Science and/or Scopus database

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