A Quantitative Study of Factors Influencing Myasthenia Gravis Telehealth Examination Score

The adoption of telemedicine is generally considered as advantageous for patients and physicians, but there is limited rigorous assessment of examination strengths and limitations. We set out to perform a quantitative assessment of the limitations of a standardized examination of subjects with myasthenia gravis (MG) during video-taped telemedicine sessions.

We utilized a video bank containing recordings from 51 MG patients who completed two telemedicine-based examinations with neuromuscular experts; each recording included the MG core examination (MG-CE) and the MG activities of daily living (MG-ADL). We then applied artificial intelligence (AI) algorithms from computer vision and speech analysis to natural language processing to generate and assess the reproducibility and inter-rater reliability of the MG-CE and MG-ADL.

We successfully developed a technology to assess video examinations. While overall MG-CE scores were consistent across examiners, individual metrics showed significant variability, with up to a 25% variation in scoring within the MG-CE's range. Additionally, there was wide variability in adherence to MG-ADL instructions. These variations were attributed to differences in examiner instructions, video recording limitations, and patient disease severity.

We were able to develop a system of digital analysis of neuromuscular examinations in order to assess variability in individual scoring measures of the MG-ADL and MG-CE. Our approach enabled post hoc quantitative analysis of neuromuscular examinations. Further refinement of this technology could enhance examiner training and reduce variability in clinical trial outcome measures.