Clinically-Validated Innovative Mobile Application for Assessing Blinking and Eyelid Movements
- URL: http://arxiv.org/abs/2511.14361v1
- Date: Tue, 18 Nov 2025 11:07:31 GMT
- Title: Clinically-Validated Innovative Mobile Application for Assessing Blinking and Eyelid Movements
- Abstract summary: Bapp is a mobile application developed using the Flutter framework and integrated with Google ML Kit for on-device, real-time analysis of eyelid movements.<n>Bapp's performance was evaluated using standard metrics, including Precision, Recall, and F1-Score, with results demonstrating 98.4% precision, 96.9% recall, and an overall accuracy of 98.3%.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Blinking is a vital physiological process that protects and maintains the health of the ocular surface. Objective assessment of eyelid movements remains challenging due to the complexity, cost, and limited clinical applicability of existing tools. This study presents the clinical validation of Bapp (Blink Application), a mobile application developed using the Flutter framework and integrated with Google ML Kit for on-device, real-time analysis of eyelid movements. The validation occurred using 45 videos from real patients, whose blinks were manually annotated by ophthalmology specialists from the Paulista School of Medicine of the Federal University of Sao Paulo (EPM-UNIFESP) to serve as the ground truth. Bapp's performance was evaluated using standard metrics, including Precision, Recall, and F1-Score, with results demonstrating 98.4% precision, 96.9% recall, and an overall accuracy of 98.3%. These outcomes confirm the reliability of Bapp as a portable, accessible, and objective tool for monitoring both normal and abnormal eyelid movements. The application offers a promising alternative to traditional manual blink counting, supporting continuous ocular health monitoring and postoperative evaluation in clinical environments.
Related papers
- Towards Clinically Interpretable Ophthalmic VQA via Spatially-Grounded Lesion Evidence [51.57735743553791]
We introduce FundusGround, a new benchmark for clinically interpretable ophthalmic VQA with spatially-grounded lesion evidence.<n>Built upon this structured lesion evidence, 72,706 questions are then generated spanning four formats.<n>Experiments demonstrate that incorporating lesion-level visual evidence consistently improves model performance and transparency.
arXiv Detail & Related papers (2026-05-21T12:37:03Z) - QuarkMedBench: A Real-World Scenario Driven Benchmark for Evaluating Large Language Models [8.050059911826338]
Large Language Models (LLMs) excel on standardized medical exams, but high scores often fail to translate to high-quality responses for real-world medical queries.<n>We introduce QuarkMedBench, an ecologically valid benchmark tailored for real-world medical LLM assessment.
arXiv Detail & Related papers (2026-03-14T01:51:43Z) - LiveClin: A Live Clinical Benchmark without Leakage [50.45415584327275]
LiveClin is a live benchmark designed for approximating real-world clinical practice.<n>We transform authentic patient cases into complex, multimodal evaluation scenarios that span the entire clinical pathway.<n>Our evaluation of 26 models on LiveClin reveals the profound difficulty of these real-world scenarios, with the top-performing model achieving a Case Accuracy of just 35.7%.
arXiv Detail & Related papers (2026-02-18T03:59:46Z) - Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models [48.95516224614331]
We introduce MedGaze-Bench, the first benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation, and diagnostic interpretation.<n>Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical, and implicit adherence to safety protocols.
arXiv Detail & Related papers (2026-01-11T02:20:40Z) - Native Intelligence Emerges from Large-Scale Clinical Practice: A Retinal Foundation Model with Deployment Efficiency [36.52215702000448]
We present ReVision, a retinal foundation model that learns from the natural alignment between 485,980 color fundus photographs and their corresponding diagnostic reports.<n>In a prospective reader study with 33 ophthalmologists, ReVision's zero-shot assistance improved diagnostic accuracy by 14.8% across all experience levels.
arXiv Detail & Related papers (2025-12-16T15:33:08Z) - EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models [82.43729208063468]
Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety.<n>We study sycophancy -- models' tendency to uncritically echo user-provided information.<n>We introduce EchoBench, a benchmark to systematically evaluate sycophancy in medical LVLMs.
arXiv Detail & Related papers (2025-09-24T14:09:55Z) - Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone [1.3060095849496556]
The way a person moves is a direct reflection of their neurological and musculoskeletal health, yet it remains one of the most underutilized vital signs in clinical practice.<n>We present our Portable Biomechanics Laboratory (PBL), which includes a secure, cloud-enabled smartphone app for data collection and a novel algorithm for fitting biomechanical models to this data.
arXiv Detail & Related papers (2025-07-11T02:29:26Z) - Evaluating Large Language Models for Multimodal Simulated Ophthalmic Decision-Making in Diabetic Retinopathy and Glaucoma Screening [37.69303106863453]
Large language models (LLMs) can simulate clinical reasoning based on natural language prompts, but their utility in ophthalmology is largely unexplored.<n>This study evaluated GPT-4's ability to interpret structured textual descriptions of retinal fundus photographs.<n>We conducted a retrospective diagnostic validation study using 300 annotated fundus images.
arXiv Detail & Related papers (2025-07-02T01:35:59Z) - Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series [3.8195510803972454]
Artefacts compromise clinical decision-making in the use of medical time series.<n>We introduce a generalised label-free framework, GenClean, for real-time artefact detection.
arXiv Detail & Related papers (2025-04-29T22:28:06Z) - Validation of Human Pose Estimation and Human Mesh Recovery for Extracting Clinically Relevant Motion Data from Videos [79.62407455005561]
Marker-less motion capture using human pose estimation produces results in-line with the results of both the IMU and MoCap kinematics.<n>While there is still room for improvement when it comes to the quality of the data produced, we believe that this compromise is within the room of error.
arXiv Detail & Related papers (2025-03-18T22:18:33Z) - Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
arXiv Detail & Related papers (2025-03-06T18:35:39Z) - Data-Centric Learning Framework for Real-Time Detection of Aiming Beam in Fluorescence Lifetime Imaging Guided Surgery [3.8261910636994925]
This study introduces a novel data-centric approach to improve real-time surgical guidance using fiber-based fluorescence lifetime imaging (FLIm)
The primary challenge arises from the complex and variable conditions encountered in the surgical environment, particularly in Transoral Robotic Surgery (TORS)
An instance segmentation model was developed using a data-centric training strategy that improves accuracy by minimizing label noise and enhancing detection robustness.
arXiv Detail & Related papers (2024-11-11T22:04:32Z) - Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments [64.59698930334012]
We present a multi-camera capture setup consisting of static and head-mounted cameras.<n>Second, we publish a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured in a surgical wet lab and a real operating theatre.<n>Third, we evaluate three state-of-the-art single-view and multi-view methods for the task of 6DoF pose estimation of surgical instruments.
arXiv Detail & Related papers (2023-05-05T13:42:19Z) - A marker-less human motion analysis system for motion-based biomarker
discovery in knee disorders [60.99112047564336]
The NHS has been having increased difficulty seeing all low-risk patients, this includes but not limited to suspected osteoarthritis (OA) patients.
We propose a novel method of automated biomarker identification for diagnosis of knee disorders and the monitoring of treatment progression.
arXiv Detail & Related papers (2023-04-26T16:47:42Z) - KIDS: kinematics-based (in)activity detection and segmentation in a
sleep case study [5.707737640557724]
Sleep behaviour and in-bed movements contain rich information on the neurophysiological health of people.
This paper proposes an online Bayesian probabilistic framework for objective (in)activity detection and segmentation based on clinically meaningful joint kinematics.
arXiv Detail & Related papers (2023-01-04T16:24:01Z) - One-shot action recognition towards novel assistive therapies [63.23654147345168]
This work is motivated by the automated analysis of medical therapies that involve action imitation games.
The presented approach incorporates a pre-processing step that standardizes heterogeneous motion data conditions.
We evaluate the approach on a real use-case of automated video analysis for therapy support with autistic people.
arXiv Detail & Related papers (2021-02-17T19:41:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.