ArticleSensors (Basel, Switzerland)2023
Eye Segmentation Method for Telehealth: Application to the Myasthenia Gravis Physical Examination.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Remote Monitoring in Myasthenia Gravis: Exploring Symptom Variability.Annals of clinical and translational neurology · 2026Trial
- Patient Communication Outside of Visits: Implications for Remote Monitoring and Digital Care Models in Myasthenia Gravis.Muscle & nerve · 2026Article
- The telemedical platform MyaLink for remote monitoring in myasthenia gravis - rationale and protocol for a proof of concept study.Journal of neuromuscular diseases · 2026Article
- A Quantitative Study of Factors Influencing Myasthenia Gravis Telehealth Examination Score.Muscle & nerve · 2025Article
- AI-Powered Telemedicine for Automatic Scoring of Neuromuscular Examinations.Bioengineering (Basel, Switzerland) · 2024Article
- Application of digital tools and artificial intelligence in the Myasthenia Gravis Core Examination.Frontiers in neurology · 2024Article
- Development and Feasibility Assessment of a Multimodal Digital Health Technology for Remote Monitoring of Symptoms in Myasthenia Gravis.Digital biomarkersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.