Artificial Intelligence for the Analysis of Workload-Related Changes in Radiologists’ Gaze Patterns

Pershin, Ilya; Kholiavchenko, Maksim; Maksudov, Bulat; Mustafaev, Tamerlan; Ibragimova, Dilyara; Ibragimov, Bulat

doi:10.1109/jbhi.2022.3183299

Cited by 8 publications

(15 citation statements)

References 46 publications

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“…Several studies have investigated the use of eye-tracking technology to detect the effects of fatigue and mental fatigue on human performance in different fields, including radiology, transportation, and cognitively demanding tasks. Pershin et al [39] use AIbased metrics to predict fatigue-related changes in radiologists' image-reading patterns. Li et al [40] propose a four-phase framework that analyzes spatial and temporal gaze patterns to assess vigilance levels in traffic controllers.…”

Section: Overview Of Related Workmentioning

confidence: 99%

“…Only frontal face images are used. The images are not captured in real-life setting Pershin et al [39] Suggested an information gain metric blending reading time, speed, and coverage…”

Section: Ref Strengths Weaknessesmentioning

confidence: 99%

“…From psychophysiological inference through categorization and adaptation, the form of adaptation will impact user perceptions of system efficacy. To instill user confidence, it must be properly developed to give timely and meaningful action or feedback at the interface [39].…”

Section: Biofeedbackmentioning

confidence: 99%

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A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

2023

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Eye gaze interfaces are an emerging technology that allows users to control graphical user interfaces (GUIs) simply by looking at them. However, using gaze-controlled GUIs can be a demanding task, resulting in high cognitive and physical load and fatigue. To address these challenges, we propose the concept and model of an adaptive human-assistive human–computer interface (HA-HCI) based on biofeedback. This model enables effective and sustainable use of computer GUIs controlled by physiological signals such as gaze data. The proposed model allows for analytical human performance monitoring and evaluation during human–computer interaction processes based on the damped harmonic oscillator (DHO) model. To test the validity of this model, the authors acquired gaze-tracking data from 12 healthy volunteers playing a gaze-controlled computer game and analyzed it using odd–even statistical analysis. The experimental findings show that the proposed model effectively describes and explains gaze-tracking performance dynamics, including subject variability in performance of GUI control tasks, long-term fatigue, and training effects, as well as short-term recovery of user performance during gaze-tracking-based control tasks. We also analyze the existing HCI and human performance models and develop an extension to the existing physiological models that allows for the development of adaptive user-performance-aware interfaces. The proposed HA-HCI model describes the interaction between a human and a physiological computing system (PCS) from the user performance perspective, incorporating a performance evaluation procedure that interacts with the standard UI components of the PCS and describes how the system should react to loss of productivity (performance). We further demonstrate the applicability of the HA-HCI model by designing an eye-controlled game. We also develop an analytical user performance model based on damped harmonic oscillation that is suitable for describing variability in performance of a PC game based on gaze tracking. The model’s validity is tested using odd–even analysis, which demonstrates strong positive correlation. Individual characteristics of users established by the damped oscillation model can be used for categorization of players under their playing skills and abilities. The experimental findings suggest that players can be categorized as learners, whose damping factor is negative, and fatiguers, whose damping factor is positive. We find a strong positive correlation between amplitude and damping factor, indicating that good starters usually have higher fatigue rates, but slow starters have less fatigue and may even improve their performance during play. The proposed HA-HCI model and analytical user performance models provide a framework for developing an adaptive human-oriented HCI that enables monitoring, analysis, and increased performance of users working with physiological-computing-based user interfaces. The proposed models have potential applications in improving the usability of future human-assistive gaze-controlled interface systems.

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Section: Overview Of Related Workmentioning

confidence: 99%

“…Only frontal face images are used. The images are not captured in real-life setting Pershin et al [39] Suggested an information gain metric blending reading time, speed, and coverage…”

Section: Ref Strengths Weaknessesmentioning

confidence: 99%

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A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

2023

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“…In radiology practice, one of the key factors that affects diagnostic accuracy is fatigue [10]. Fatigue can reduce cognitive ability and attention lapses, decrease vigilance, and change gaze patterns of radiologists [10], [11]. In the event of fatigue, the computer generated saliency that represents the standard image reading can be used to assist the radiologist during diagnosis to avoid potential errors caused by fatigue.…”

Section: Introductionmentioning

confidence: 99%

Predicting Radiologists' Gaze With Computational Saliency Models in Mammogram Reading

Lou

Young

White

et al. 2024

IEEE Trans. Multimedia

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Previous studies have shown that there is a strong correlation between radiologists' diagnoses and their gaze when reading medical images. The extent to which gaze is attracted by content in a visual scene can be characterised as visual saliency. There is a potential for the use of visual saliency in computer-aided diagnosis in radiology. However, little is known about what methods are effective for diagnostic images, and how these methods could be adapted to address specific applications in diagnostic imaging. In this study, we investigate 20 state-of-the-art saliency models including 10 traditional models and 10 deep learning-based models in predicting radiologists' visual attention while reading 196 mammograms. We found that deep learning-based models represent the most effective type of methods for predicting radiologists' gaze in mammogram reading; and that the performance of these saliency models can be significantly improved by transfer learning. In particular, an enhanced model can be achieved by pre-training the model on a large-scale natural image saliency dataset and then finetuning it on the target medical image dataset. In addition, based on a systematic selection of backbone networks and network architectures, we proposed a parallel multi-stream encoded model which outperforms the state-of-the-art approaches for predicting saliency of mammograms.

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“…The reason why eye tracking research is so important is that it reflects physicians’ perceptual processes, that is, information capture that they are not cognitively aware of. For example, physicians cannot be aware that the quality of their image reading abilities deteriorates due to fatigue, but an eye-tracking framework can capture the reduction of anatomical coverage with the gaze – a factor that has been associated with fatigue – and alert the reader [ 13 ], [ 14 ]. As the reading of most medical images is guided by perception, eye tracking is the only way to monitor how observers interact with the image.…”

Section: Introductionmentioning

confidence: 99%

The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review

Ibragimov,

Mello-Thoms

2024

IEEE J. Biomed. Health Inform.

Self Cite

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Machine learning (ML) has revolutionized medical image-based diagnostics. In this review, we cover a rapidly emerging field that can be potentially significantly impacted by MLeye tracking in medical imaging. The review investigates the clinical, algorithmic, and hardware properties of the existing studies. In particular, it evaluates 1) the type of eye-tracking equipment used and how the equipment aligns with study aims; 2) the software required to record and process eye-tracking data, which often requires user interface development, and controller command and voice recording; 3) the ML methodology utilized depending on the anatomy of interest, gaze data representation, and target clinical application. The review concludes with a summary of recommendations for future studies, and confirms that the inclusion of gaze data broadens the ML applicability in medical imaging from computer-aided diagnosis (CAD) to gaze-based image annotation, physicians' error detection, fatigue recognition, and other areas of potentially high research and clinical impact.

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Artificial Intelligence for the Analysis of Workload-Related Changes in Radiologists’ Gaze Patterns

Cited by 8 publications

References 46 publications

A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

Predicting Radiologists' Gaze With Computational Saliency Models in Mammogram Reading

The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review

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