Introduction/aims: 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.
Methods: 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.
Results: 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.
Discussion: 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.
Keywords: artificial intelligence; computer vision; human factor; neurological examination myasthenia gravis; telemedicine.
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