Eye Segmentation Method for Telehealth: Application to the Myasthenia Gravis Physical Examination.

Due to the precautions put in place during the COVID-19 pandemic, utilization of telemedicine has increased quickly for patient care and clinical trials. Unfortunately, teleconsultation is closer to a video conference than a medical consultation, with the current solutions setting the patient and doctor into an evaluation that relies entirely on a two-dimensional view of each other. We are developing a patented telehealth platform that assists with diagnostic testing of ocular manifestations of myasthenia gravis. We present a hybrid algorithm combining deep learning with computer vision to give quantitative metrics of ptosis and ocular muscle fatigue leading to eyelid droop and diplopia. The method works both on a fixed image and frame by frame of the video in real-time, allowing capture of dynamic muscular weakness during the examination. We then use signal processing and filtering to derive robust metrics of ptosis and l ocular misalignment. In our construction, we have prioritized the robustness of the method versus accuracy obtained in controlled conditions in order to provide a method that can operate in standard telehealth conditions. The approach is general and can be applied to many disorders of ocular motility and ptosis.

Application of Digital Tools and Artificial Intelligence to the Myasthenia Gravis Core Examination.

Background: Advances in video image analysis and artificial intelligence provide the opportunity to transform the approach to patient evaluation through objective digital evaluation. Objectives: We assessed ability to quantitate Zoom video recordings of a standardized neurological examination the myasthenia gravis core examination (MG-CE), which had been designed for telemedicine evaluations. Methods: We used Zoom (Zoom Video Communications) videos of patients with myasthenia gravis undergoing the MG-CE. Computer vision in combination with artificial intelligence methods were used to build algorithms to analyze videos with a focus on eye or body motions. For the assessment of examinations involving vocalization, signal processing methods were developed, including natural language processing. A series of algorithms were built that could automatically compute the metrics of the MG-CE. Results: Fifty-one patients with MG with videos recorded twice on separate days and 15 control subjects were assessed once. We were successful in quantitating lid, eye, and arm positions and as well as well as develop respiratory metrics using breath counts. Cheek puff exercise was found to be of limited value for quantitation. Technical limitations included variations in illumination, bandwidth, and recording being done on the examiner side, not the patient. Conclusions: Several aspects of the MG-CE can be quantitated to produce continuous measures via standard Zoom video recordings. Further development of the technology offer the ability for trained, non-physician, health care providers to perform precise examination of patients with MG outside the clinic, including for clinical trials. Plain Language Summary: Advances in video image analysis and artificial intelligence provide the opportunity to transform the approach to patient evaluation. Here, we asked whether video recordings of the typical telemedicine examination for the patient with myasthenia gravis be used to quantitate examination findings? Despite recordings not made for purpose, we were able to develop and apply computer vision and artificial intelligence to Zoom recorded videos to successfully quantitate eye muscle, facial muscle, and limb fatigue. The analysis also pointed out limitations of human assessments of bulbar and respiratory assessments. The neuromuscular examination can be enhanced by advance technologies, which have the promise to improve clinical trial outcome measures as well as standard care.

A Digital Telehealth System to Compute the Myasthenia Gravis Core Examination Metrics.

Background: Telemedicine practice for neurological diseases has grown significantly during the COVID-19 pandemic.Telemedicine offers an opportunity to assess digitalization of examinations and enhances access to modern computer vision and artificial intelligence processing to annotate and quantify examinations in a consistent and reproducible manner. The Myasthenia Gravis Core Examination (MG-CE) has been recommended for the telemedicine evaluation of patients with myasthenia gravis. Objective: We aimed to assess the ability to take accurate and robust measurements during the examination, which would allow improvement in workflow efficiency by making the data acquisition and analytics fully automatic and thereby limit the potential for observation bias. Methods: We used Zoom (Zoom Video Communications) videos of patients with myasthenia gravis undergoing the MG-CE. The core examination tests required 2 broad categories of processing. First, computer vision algorithms were used to analyze videos with a focus on eye or body motions. Second, for the assessment of examinations involving vocalization, a different category of signal processing methods was required. In this way, we provide an algorithm toolbox to assist clinicians with the MG-CE. We used a data set of 6 patients recorded during 2 sessions. Results: Digitalization and control of quality of the core examination are advantageous and let the medical examiner concentrate on the patient instead of managing the logistics of the test. This approach showed the possibility of standardized data acquisition during telehealth sessions and provided real-time feedback on the quality of the metrics the medical doctor is assessing. Overall, our new telehealth platform showed submillimeter accuracy for ptosis and eye motion. In addition, the method showed good results in monitoring muscle weakness, demonstrating that continuous analysis is likely superior to pre-exercise and post-exercise subjective assessment. Conclusions: We demonstrated the ability to objectively quantitate the MG-CE. Our results indicate that the MG-CE should be revisited to consider some of the new metrics that our algorithm identified. We provide a proof of concept involving the MG-CE, but the method and tools developed can be applied to many neurological disorders and have great potential to improve clinical care.