Continuous Non-Invasive Eye Tracking In Intensive Care
- URL: http://arxiv.org/abs/2108.01439v1
- Date: Fri, 23 Jul 2021 16:19:35 GMT
- Title: Continuous Non-Invasive Eye Tracking In Intensive Care
- Authors: Ahmed Al-Hindawi, Marcela Paula Vizcaychipi, Yiannis Demiris
- Abstract summary: Delirium, an acute confusional state, is a common occurrence in Intensive Care Units.
Current diagnostic methods have several limitations leading to the suggestion of eye-tracking for its diagnosis through in-attention.
The system is the first eye-tracking system to be deployed in an ICU.
- Score: 31.348963275486494
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Delirium, an acute confusional state, is a common occurrence in Intensive
Care Units (ICUs). Patients who develop delirium have globally worse outcomes
than those who do not and thus the diagnosis of delirium is of importance.
Current diagnostic methods have several limitations leading to the suggestion
of eye-tracking for its diagnosis through in-attention. To ascertain the
requirements for an eye-tracking system in an adult ICU, measurements were
carried out at Chelsea & Westminster Hospital NHS Foundation Trust. Clinical
criteria guided empirical requirements of invasiveness and calibration methods
while accuracy and precision were measured. A non-invasive system was then
developed utilising a patient-facing RGB-camera and a scene-facing RGBD-camera.
The system's performance was measured in a replicated laboratory environment
with healthy volunteers revealing an accuracy and precision that outperforms
what is required while simultaneously being non-invasive and calibration-free
The system was then deployed as part CONfuSED, a clinical feasibility study
where we report aggregated data from 5 patients as well as the acceptability of
the system to bedside nursing staff. The system is the first eye-tracking
system to be deployed in an ICU.
Related papers
- AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence [4.494833548150712]
We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients.<n>Powered by UniTS, a state-of-the-art (SoTA) universal time-series model, our framework autonomously learns each patient's unique physiological and behavioral patterns.
arXiv Detail & Related papers (2025-08-05T13:24:15Z) - Metrics that matter: Evaluating image quality metrics for medical image generation [48.85783422900129]
This study comprehensively assesses commonly used no-reference image quality metrics using brain MRI data.<n>We evaluate metric sensitivity to a range of challenges, including noise, distribution shifts, and, critically, morphological alterations designed to mimic clinically relevant inaccuracies.
arXiv Detail & Related papers (2025-05-12T01:57:25Z) - An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data [0.30723404270319693]
This work proposes a three stage approach for automated continuous grading of knee OA.
It learns a robust representation of healthy knee X-rays and grading disease severity based on its distance to the centre of normality.
The proposed methodology outperforms existing techniques by margins of up to 24% in terms of OA detection and the disease severity scores correlate with the Kellgren-Lawrence grading system at the same level as human expert performance.
arXiv Detail & Related papers (2024-07-16T08:37:33Z) - Noninvasive Acute Compartment Syndrome Diagnosis Using Random Forest
Machine Learning [0.0]
Acute compartment syndrome (ACS) is an orthopedic emergency, caused by elevated pressure within a muscle compartment.
This study proposes an objective and noninvasive diagnostic for ACS.
The device detects ACS through a random forest machine learning model that uses surrogate pressure readings from force-sensitive resistors (FSRs) placed on the skin.
arXiv Detail & Related papers (2024-01-18T21:49:04Z) - Evaluating AI systems under uncertain ground truth: a case study in dermatology [43.8328264420381]
We show that ignoring uncertainty leads to overly optimistic estimates of model performance.
In skin condition classification, we find that a large portion of the dataset exhibits significant ground truth uncertainty.
arXiv Detail & Related papers (2023-07-05T10:33:45Z) - Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement [61.28459114068828]
We propose an intraoperative planning approach for robotic spine surgery that leverages real-time observation for drill path planning based on Safe Deep Reinforcement Learning (DRL)
Our approach was capable of achieving 90% bone penetration with respect to the gold standard (GS) drill planning.
arXiv Detail & Related papers (2023-05-09T11:42:53Z) - AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing [2.7503982558916906]
The intensive care unit (ICU) is a specialized hospital space where critically ill patients receive intensive care and monitoring.
Comprehensive monitoring is imperative in assessing patients conditions, in particular acuity, and ultimately the quality of care.
Currently, visual assessments for acuity, including fine details such as facial expressions, posture, and mobility, are sporadically captured, or not captured at all.
arXiv Detail & Related papers (2023-03-11T00:25:55Z) - Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.
By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation.
DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
arXiv Detail & Related papers (2023-01-01T05:02:46Z) - Reducing a complex two-sided smartwatch examination for Parkinson's
Disease to an efficient one-sided examination preserving machine learning
accuracy [63.20765930558542]
We have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD)
This study provided the largest PD sample size of two-hand synchronous smartwatch measurements.
arXiv Detail & Related papers (2022-05-11T09:12:59Z) - The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients [31.567542945171834]
We describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge.
BraTS-Reg is the first public benchmark environment for deformable registration algorithms.
The aim of BraTS-Reg is to continue to serve as an active resource for research.
arXiv Detail & Related papers (2021-12-13T19:25:16Z) - Dual-Consistency Semi-Supervised Learning with Uncertainty
Quantification for COVID-19 Lesion Segmentation from CT Images [49.1861463923357]
We propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images.
Our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins.
arXiv Detail & Related papers (2021-04-07T16:23:35Z) - An Interpretable Multiple-Instance Approach for the Detection of
referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images.
By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy.
We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
arXiv Detail & Related papers (2021-03-02T13:14:15Z) - Personalized Deep Learning for Ventricular Arrhythmias Detection on
Medical IoT Systems [17.966382901357118]
Life-threatening ventricular arrhythmias (VA) are the leading cause of sudden cardiac death (SCD)
We propose the personalized computing framework for deep learning based VA detection on medical IoT systems.
We equip the system with real-time inference on both intracardiac and surface rhythm monitors.
arXiv Detail & Related papers (2020-08-18T17:41:58Z) - Hemogram Data as a Tool for Decision-making in COVID-19 Management:
Applications to Resource Scarcity Scenarios [62.997667081978825]
COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure.
This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients.
Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
arXiv Detail & Related papers (2020-05-10T01:45:03Z)
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.