Analysis of Machine Learning Assisted Fatigue Identification in Radiology Readings

Fatigue in radiology is a readily studied area. Machine learning concepts appliedto the identification of fatigue are also readily available. However, the intersection between the two areas is not a relative commonality. This study looks to explore the intersection of fatigue in radiology and machine learning concepts by analyzing temporal trends in multivariate time series data. A novel methodological approach using support vector machines to observe temporal trends in time-based aggregations of time series data is proposed. The data used in the study is captured in a real-world, unconstrained radiology setting where gaze and facial metrics are captured from radiologists performing live image reviews. The captured data is formatted into classes whose labels represent a window of time during the radiologist’s review. Using the labeled classes, the decision function and accuracy of trained, linear support vector machine models are evaluated to produce a visualization of temporal trends and critical inflection points as well as the contribution of individual features. Consequently, the study finds valid potential justification in the methods suggested. The study offers a prospective use of maximummargin classification to demarcate the manipulation of an abstract phenomenon such as fatigue on temporal data. Potential applications are envisioned that could improve the workload distribution of the medical act.