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.
SurgCalib: Gaussian Splatting-Based Hand-Eye Calibration for Robot-Assisted Minimally Invasive Surgery
Robotics and Control Laboratory, University of British Columbia（机器人与控制实验室，不列颠哥伦比亚大学）；Department of Biomedical Engineering, National University of Singapore（生物医学工程系，新加坡国立大学）；Division of Otolaryngology – Head and Neck Surgery, University of British Columbia（耳鼻喉科–头颈外科部，不列颠哥伦比亚大学）
Depth-induced Saliency Comparison Network for Diagnosis of Alzheimer's Disease via Jointly Analysis of Visual Stimuli and Eye Movements
Tianjin University（天津大学）；Zhejiang International Institute of Innovative Design（浙江国际创新设计研究院）；International Institute for Innovative Design and Intelligent Manufacturing of Tianjin University（天津大学创新设计与智能制造国际研究院）；China University of Geosciences（中国地质大学）；Lehigh University（利哈伊大学）；Tianjin Huanhu Hospital（天津环湖医院）；Tianjin Dementia Institute（天津痴呆症研究院）
FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）
Chronic wounds including diabetic and arterial/venous insufficiency injuries have become a major burden for healthcare systems worldwide. Demographic changes suggest that wound care will play an even bigger role in the coming decades. Predicting and monitoring response to therapy in wound care is currently largely based on visual inspection with little information on the underlying tissue. Thus, there is an urgent unmet need for innovative approaches that facilitate personalized diagnostics and treatments at the point-of-care. It has been recently shown that ultrasound imaging can monitor resp…
An integrated language-vision foundation model for conversational diagnostics and triaging in primary eye care
Singapore Eye Research Institute, Singapore National Eye Centre（新加坡眼科研究所，新加坡国家眼科中心）；Institute of High Performance Computing, Agency of Science, Technology and Research（高性能计算研究所，新加坡科技研究局）；University of Pennsylvania（宾夕法尼亚大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Columbia University（哥伦比亚大学）；National University of Singapore（新加坡国立大学）；Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）；Shanghai Eye Disease Prevention & Treatment Center, Shanghai Eye Hospital（上海市眼病防治中心，上海市眼医院）
Princeton University（普林斯顿大学）；Harvard Medical School（哈佛医学院）；Brigham & Women’s Hospital（布莱根妇女医院）
Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow. Therefore, existing saliency prediction datasets are order-of-magnitudes smaller than typical datasets for other vision recognition tasks. The small size of these datasets limits the potential for training data intensive algorithms, and causes overfitting in benchmark evaluation. To address this deficiency, this paper introduces a webcam-based gaze tracking system that supports large-scale, crowdsourced eye tracking deployed on Amazon Mechanical Turk (AMT…
Among the most impactful diabetic complications are diabetic retinopathy, the leading cause of blindness among working class adults, and cardiovascular disease, the leading cause of death worldwide. This study describes the development of improved machine learning based screening of these conditions. First, a random forest model was developed by retrospectively analyzing the influence of various risk factors (obtained quickly and non-invasively) on cardiovascular risk. Next, a deep-learning model was developed for prediction of diabetic retinopathy from retinal fundus images by a modified and …
Tianjin University（天津大学）；Kunming University of Science and Technology（昆明理工大学）；Tianjin Dementia Institute（天津痴呆研究所）；Tianjin Huanhu Hospital（天津环湖医院）
Alzheimer's Disease (AD) causes a continuous decline in memory, thinking, and judgment. Traditional diagnoses are usually based on clinical experience, which is limited by some realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as typical eye-tracking behavior, is of great clinical value to detect cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conduct a 3D comprehensive visual task on a non-invasive eye-trackin…
GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction
Department of Psychology, University of Miami（迈阿密大学心理学系）；Northeastern University（东北大学）；Ecole Normale Supérieure, PSL University, EHESS, CNRS（巴黎高等师范学院（PSL大学）、EHESS、CNRS）；International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study（神经智能国际研究中心（WPI-IRCN）、东京大学高级研究机构）
This paper revisits recognition of natural image pleasantness by employing deep convolutional neural networks and affordable eye trackers. There exist several approaches to recognize image pleasantness: (1) computer vision, and (2) psychophysical signals. For natural images, computer vision approaches have not been as successful as for abstract paintings and is lagging behind the psychophysical signals like eye movements. Despite better results, the scalability of eye movements is adversely affected by the sensor cost. While the introduction of affordable sensors have helped the scalability is…
Continuous affect prediction involves the discrete time-continuous regression of affect dimensions. Dimensions to be predicted often include arousal and valence. Continuous affect prediction researchers are now embracing multimodal model input. This provides motivation for researchers to investigate previously unexplored affective cues. Speech-based cues have traditionally received the most attention for affect prediction, however, non-verbal inputs have significant potential to increase the performance of affective computing systems and in addition, allow affect modelling in the absence of sp…
Univ. of California Santa Barbara（加州大学圣巴巴拉分校）；West Virginia University（西弗吉尼亚大学）；Santa Barbara Cottage Hospital（圣巴巴拉 Cottage 医院）
Manual analysis of body poses of bed-ridden patients requires staff to continuously track and record patient poses. Two limitations in the dissemination of pose-related therapies are scarce human resources and unreliable automated systems. This work addresses these issues by introducing a new method and a new system for robust automated classification of sleep poses in an Intensive Care Unit (ICU) environment. The new method, coupled-constrained Least-Squares (cc-LS), uses multimodal and multiview (MM) data and finds the set of modality trust values that minimizes the difference between expect…
Developing a Dyslexia Indicator Using Eye Tracking
School of Computing, Dublin City University, Ireland（都柏林城市大学计算学院）；Ho Chi Minh City Open University（胡志明市开放大学）；Insight Centre for Data Analytics, Dublin City University, Ireland（数据分析洞察中心）
Oblique back-illumination capillaroscopy has recently been introduced as a method for high-quality, non-invasive blood cell imaging in human capillaries. To make this technique practical for clinical blood cell counting, solutions for automatic processing of acquired videos are needed. Here, we take the first step towards this goal, by introducing a deep learning multi-cell tracking model, named CycleTrack, which achieves accurate blood cell counting from capillaroscopic videos. CycleTrack combines two simple online tracking models, SORT and CenterTrack, and is tailored to features of capillar…
Discrimination of Radiologists Utilizing Eye-Tracking Technology and Machine Learning: A Case Study
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Northwestern University（西北大学）；University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；Long School of Medicine（朗医学院）
Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis
College of Engineering and Information Technology, University of Dubai, UAE（工程与信息技术学院，迪拜大学）；School of Electrical, Computer, and Telecommunications Engineering, University of Wollongong, Australia（电气、计算机与电信工程学院，沃尔longong大学）；College of Medicine and Health Sciences, Mohammed Bin Rashid University Dubai, UAE（医学与健康科学学院，迪拜穆罕默德·本·拉希德大学）
Fudan University（复旦大学）；The Fifth Hospital of Shijiazhuang, Hebei Medical University（河北医科大学石家庄市第五医院）；Hubei Provincial Hospital of Traditional Chinese Medicine（湖北省中医院）；Shanghai Public Health Clinical Center, Fudan University（复旦大学附属上海市公共卫生临床中心）；Aimomics (Shanghai) Intelligent Technology Co., Ltd.（艾摩米斯（上海）智能科技有限公司）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院）
It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts. On the other hand, there are more and more ocular manifestations that have been reported in the COVID-19 patients as growing clinical evidence[1]. This inspired this project. We have conducted the joint clinical research since January 2021 at the ShiJiaZhuang City, Heibei province, China, which approved by the ethics committee of The fifth hospital of ShiJiaZhuang of Hebei M…
Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trust
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Yong Loo Lin School of Medicine, NUS（新加坡国立大学杨潞龄医学院）
Non-Invasive Detection of PROState Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI
University of Chile（智利大学）；Clínica Dávila（达维拉诊所）；Zhejiang University（浙江大学）；Hospital Clinic, Universitat de Barcelona（巴塞罗那大学医院诊所）；San Sebastián University（圣塞巴斯蒂安大学）
Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；International Max Planck Research School for Intelligent Systems (IMPRS-IS)（国际马克斯·普朗克智能系统研究院（IMPRS-IS））；University of Stuttgart（斯图加特大学）；University of Arkansas（阿肯色大学）；Oldenburg University（奥尔登堡大学）
本文综述深度学习在糖尿病视网膜病变、青光眼等眼科疾病诊断与治疗中的前沿应用、主流架构及临床落地挑战，并展望多模态 AI 眼保健的发展方向。
School of Engineering, University of Maryland（马里兰大学工程学院）；College of Engineering, Qatar University（卡塔尔大学工程学院）；University of Dhaka（达卡大学）
In the 34 developed and 156 developing countries, there are about 132 million disabled people who need a wheelchair constituting 1.86% of the world population. Moreover, there are millions of people suffering from diseases related to motor disabilities, which cause inability to produce controlled movement in any of the limbs or even head.The paper proposes a system to aid people with motor disabilities by restoring their ability to move effectively and effortlessly without having to rely on others utilizing an eye-controlled electric wheelchair. The system input was images of the users eye tha…
