Enhancing Myasthenia Gravis Care With AI-Powered Telemedicine

Inteleclinic, a new digital artificial intelligence (AI) tool that automatically analyzes and scores neuromuscular examinations conducted via telemedicine using artificial intelligence (AI) may offer a solution to the challenges of the variability of assessments and monitoring of patients with neuromuscular disorders, particularly myasthenia gravis, according to a new study published in Bioengineering (Basel).¹

"This study is significant in terms of enabling more complex data collection while providing reproducibility of scores and facilitating comparison with previous patient visits in an objective fashion while providing easy access and shorter time on both patient and provider ends," the authors note.

The researchers validated their AI-powered solution using 102 telemedicine videos from 51 patients who have myasthenia gravis, which included assessments done by 8 board-certified neurologists from 5 academic hospitals across the US. The AI algorithm segmented each video examination into relevant clips corresponding to the Myasthenia Gravis Core Exam (MG-CE) and then automatically generate a score based on established criteria. Each patient underwent 2 assessments within a week, except 1 patient with a 39-day interval.

In this analysis, the AI system excelled at recognizing and scoring tests related to ptosis, diplopia, and arm strength. | Image Credit: © NicoElNino-stock.adobe.com

The AI-powered system significantly reduced the time required to score the neuromuscular exams. Traditionally, manual scoring of MG-CE exams can be time intensive, but the AI system automates this process by segmenting the telemedicine videos and analyzing each clip.² A clinician interface is generated and includes features for efficient video review, allowing rapid playback focused on test segments, and provides annotated landmarks to highlight relevant anatomical features for scoring accuracy. It also identifies potential scoring errors in cases where the algorithm encounters difficulties to aid neurologists in quickly making informed decisions.

The system's automated video segmentation and scoring streamlined the evaluation process also ensures that the assessment is consistent across different examiners and settings, eliminating the variability inherent in human-administered tests. In particular, the AI system excelled at recognizing and scoring tests related to ptosis, diplopia, and arm strength, and was able to identify gaze direction frames from the segmented videos with a 97% success rate. In ptosis, 51 of 86 tests were successfully segmented and scored, while 30 of left eye and 31 of right eye diplopia tests were successfully scored. In arm strength, 82 of the 86 videos were successfully scored.

"The results indicate that our approach yields outcomes that are consistently comparable to those of expert clinicians for the majority of examination metrics under standard clinical conditions," the authors report. Specifically, the system consistently evaluated key myasthenia gravis domains, including ocular, bulbar, respiratory, and limb function.

The study's findings have broader implications for using AI in clinical settings, including clinical trials, the researchers noted, and the AI system offers a solution to this problem by providing uniform, objective scoring across all patients, regardless of the clinician or location.

"Clinical trial outcome measures for many neurological diseases are compromised by subjectivity, poor reproducibility across evaluators, and the need for in-person evaluations,” they explained.

In addition to its potential in clinical trials, the AI system enables more frequent and reliable monitoring of patients with myasthenia gravis. Regular, remote evaluations can provide clinicians with valuable data over time, helping them track disease progression and adjust treatment plans as needed. It also is particularly useful in rural or underserved areas, where access to neurologists is limited.

"We believe that the lessons learned from our work will advance the field of computing and improve quantitative patient clinical assessments. We also hope that our digitally robust and quantitative assessment telemedicine system will improve patient care in low-resource areas with limited access to neurologists," the study’s authors added.

References

1. Lesport Q, Palmie D, Öztosun G, Kaminski HJ, Garbey M. AI-powered telemedicine for automatic scoring of neuromuscular examinations. Bioengineering (Basel). 2024;11(9):942. doi:10.3390/bioengineering11090942

2. Guidon AC, Muppidi S, Nowak RJ, et al. Telemedicine visits in myasthenia gravis: expert guidance and the Myasthenia Gravis Core Exam (MG-CE). Muscle Nerve. 2021;64(3):270-276. doi:10.1002/mus.272v