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

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.

Keywords:

• Amyotrophic lateral sclerosis
• ALS
• artificial intelligence
• AI
• diagnostic
• sensitivity
• specificity

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Author contributions

TPU and NJ conceptualized the present study while all authors were responsible for different sections of methodology, data collection, result analysis, software, formal analysis, data curation, validation, and investigation. Initial draft preparation and visualizations were done by TPU, MP, RSS, JFA, and MM. NJ was responsible for statistical analyses and critical revision of the search strategy. All authors were responsible for revising the manuscript. Project administration and resource management was done by TPU and NJ. Supervision was done by AK. All authors have read and agreed to the final version for publication.

Availability of data

Data generated in the present study is presented within the results section of the paper.