Improving X-ray Diagnostics through Eye-Tracking and XR

被引:7
作者
Moreira, Catarina [1 ]
Nobre, Isabel Blanco [2 ]
Sousa, Sandra Costa [2 ]
Pereira, Joao Madeiras [3 ]
Jorge, Joaquim [3 ]
机构
[1] Queensland Univ Technol, Sch Informat Syst, INESC ID, Brisbane, Qld, Australia
[2] Grp Lusiadas, Imagiol Dept, Lisbon, Portugal
[3] Univ Lisbon, Inst Super Tecn, INESC ID, Lisbon, Portugal
来源
2022 IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES ABSTRACTS AND WORKSHOPS (VRW 2022) | 2022年
关键词
Applied Computing; Life and medical Sciences; Health informatics Computing methodologies; Artificial intelligence; Knowledge representation and reasoning; Causal reasoning and diagnostics Human-centered computing; Virtual reality; FATIGUE;
D O I
10.1109/VRW55335.2022.00099
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
There is a growing need to assist radiologists in performing X-ray readings and diagnoses fast, comfortably, and effectively. As radiologists strive to maximize productivity, it is essential to consider the impact of reading rooms in interpreting complex examinations and ensure that higher volume and reporting speeds do not compromise patient outcomes. Virtual Reality (VR) is a disruptive technology for clinical practice in assessing X-ray images. We argue that conjugating eye-tracking with VR devices and Machine Learning may overcome obstacles posed by inadequate ergonomic postures and poor room conditions that often cause erroneous diagnostics when professionals examine digital images.
引用
收藏
页码:441 / 444
页数:4
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