PURPOSE: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists' gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. METHODS: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. RESULTS: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. CONCLUSIONS: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists' gaze behavior and image content.
PURPOSE: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists' gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. METHODS: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. RESULTS: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. CONCLUSIONS: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists' gaze behavior and image content.
Authors: Jacqueline A Griffin; Kevin Carr; Kerrin Bersani; Nicholas Piniella; Daniel Motta-Calderon; Maria Malik; Alison Garber; Kumiko Schnock; Ronen Rozenblum; David W Bates; Jeffrey L Schnipper; Anuj K Dalal Journal: Diagnosis (Berl) Date: 2021-08-23
Authors: Abdulla Al Suman; Carlo Russo; Ann Carrigan; Patrick Nalepka; Benoit Liquet-Weiland; Robert Ahadizad Newport; Poonam Kumari; Antonio Di Ieva Journal: PLoS One Date: 2021-12-02 Impact factor: 3.240
Authors: Felipe Costa Moreira; André Yui Aihara; Henrique Manoel Lederman; Ivan Torres Pisa; Josceli Maria Tenório Journal: Radiol Bras Date: 2018 Sep-Oct