通过眼动追踪和图像分析预测放射诊断中的错误：在乳腺 X 光摄影中的初步研究。

Predicting diagnostic error in radiology via eye-tracking and image analytics: preliminary investigation in mammography.

机构信息

Biomedical Science and Engineering Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831.

出版信息

Med Phys. 2013 Oct;40(10):101906. doi: 10.1118/1.4820536.

DOI:10.1118/1.4820536

PMID:24089908

Abstract

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.

摘要

目的

本研究的主要目的是通过合并放射科医生的注视行为和图像特征来测试预测乳房 X 光摄影诊断错误的可行性。次要目的是研究基于群组和个性化的预测模型，用于不同经验水平的放射科医生。

方法

该研究针对评估乳房 X 光摄影肿块恶性可能性的临床任务进行。作为 IRB 批准的试点研究的一部分，从四名放射科住院医师和两名乳房成像专家那里获得了 40 个病例的眼动追踪数据和诊断决策。从眼动追踪数据中提取注视行为特征。从图像中提取计算机生成的和 BIRADS 图像特征。最后，使用机器学习算法合并注视和图像特征来预测人为错误。深入探讨了特征选择，以确定各种特征的相对贡献。还研究了基于群组和个性化的用户建模。

结果

通过合并放射科医生的注视行为特征和正在审查的图像的纹理特征，机器学习可用于预测诊断错误。利用从多个读者收集的数据产生了一个合理的群组模型[ROC 曲线下面积（AUC）= 0.792 ± 0.030]。对于经验更丰富的读者（AUC = 0.837 ± 0.029），个性化用户建模比经验较少的读者（AUC = 0.667 ± 0.099）更为准确。表现最佳的基于群组和个性化的预测模型涉及注视和图像特征的组合。