Novel Perceptual and Oculomotor Heuristics for Enhancing Radiologic Performance

计划摘要 放射成像通常是许多破坏性疾病诊断途径的第一步；因此，错误地评估“正常”可能会导致死亡。虽然图像中的灰度对象可以通过其一阶图像统计量来描述--如对比度、空间频率、位置、熵和方位--但这些维度本身并不能指示异常与正常的放射学发现。我们是一个高度多样化的团队，提出了一种经验方法来确定一阶统计量的混合--“视觉纹理”--放射学专家明确和隐含地使用这种方法来识别医学图像中潜在异常的位置。我们的创新方法不依赖于假设哪些纹理对异常检测可能重要或可能不重要。相反，我们将跟踪专家放射科医生的眼球运动行为，以确定他们有意识和无意识的靶向选择，从而确定哪些纹理是经验性的。放射科专家快速发现异常的能力表明，他们或许能够首先在视网膜外围识别出异常。因此，外围视觉分析技能对无线电逻辑性能具有潜在的关键作用，尽管研究还不够深入。我们将测量这些技能，并利用结果来开发感知学习启发式方法，以改进外围异常纹理检测。通过比较新手和专家，我们将确定第一个是不专业的，是因为对诊断相关的纹理缺乏敏感性(纹理信息性)，还是因为缺乏关于哪些纹理是异常的知识，或者是由于缺乏敏感性和知识的组合。放射学还需要通过实践和优化获得眼球运动技能。因此，放射学专业知识以可预测和可检测的方式改变眼球运动系统，就像运动员的身体和大脑随着运动中专业知识的获得而发生变化一样。因此，我们将分析专家在医学图像中的注视选择之间的一致性，以及专家和新手放射科医生的眼动表现特征，以创建一个客观的眼球运动双标记物。新手和专家之间的差异将训练一个深度学习(DL)系统，该系统将具有人类视觉和动眼运动的表现特征。使用由专家放射科专家小组确定的异常情况对DL进行培训，将使其能够以模拟人类放射科医生以最高准确度、精确度和速度执行的方式精确定位可能的解决方案。由此得到的可能的最佳和次优图像阅读策略的排名排序列表将作为一个基准工具，量化实际的临床医生和住院医生在休息和疲劳情况下阅读相同图像的表现。测量训练和疲劳对放射学专业知识的影响将是绩效评估中跨学科的重大进步。我们提出的用专业知识流失来量化疲劳的建议，代表着在医学和其他领域朝着客观的职责适宜性和专业知识衡量标准迈出的变革性的一步。

PROGRAM SUMMARY Radiological imaging is often the first step of the diagnostic pathway for many devastating diseases; thus, an erroneous assessment of “normal” can lead to death. Whereas a grayscale object in an image can be described by its first-order image statistics—such as contrast, spatial frequency, position, entropy, and orientation—none of these dimensions, by itself, indicates abnormal vs normal radiological findings. We are a highly diverse team proposing an empirical approach to determine the mixtures of the first-order statistics—the “visual textures”— that radiology experts explicitly and implicitly use to identify the locations of potential abnormalities in medical images. Our innovative approach does not rely on assumptions about which textures may or may not be im-portant to abnormality detection. Instead, we will track the oculomotor behavior of expert radiologists to deter-mine their conscious and unconscious targeting choices, and thus ascertain which textures are empirically in-formative. The ability of expert radiologists to rapidly find abnormalities suggests that they may be able to first identify them in their retinal periphery. Peripheral visual analysis skills are therefore potentially critical to radio-logic performance, despite being understudied. We will measure these skills and leverage the results to develop perceptual learning heuristics to improve peripheral abnormality texture detection. By comparing novices to ex-perts we will determine whether the first are inexpert due to a lack of sensitivity to diagnostically relevant textures (texture informativeness), or to a lack of knowledge about which textures are abnormal, or to a combined lack of both sensitivity and knowledge. Radiology also requires the acquisition of oculomotor skills through practice and optimization. Radiologic expertise thus changes the oculomotor system in predictable and detectable ways, in much the same way that an athlete’s body and brain change as a function of expertise acquisition in their sport. We will therefore analyze both the consistency between experts’ fixation choices in medical images, and the eye movement performance characteristics of experts vs novice radiologists, to create an objective oculomotor bi-omarker of radiological expertise. The differences between novices and experts will train a deep learning (DL) system, which will have human visual and oculomotor performance characteristics. Training the DL with the abnormalities identified by a panel of expert radiologists will allow it to pinpoint the possible solutions in the manner of a simulated human radiologist performing at peak accuracy, precision, and speed. The resulting rank-ordered list of possible optimal and suboptimal image-reading strategies will serve as a benchmarking tool to quantify the performance of actual clinicians and residents who read the same images, rested vs fatigued. Meas-uring the effects of both training and fatigue on radiology expertise will be a major interdisciplinary cross-cutting advance in performance assessment. Our proposal to quantify fatigue in terms of erosion of expertise represents a transformational advance towards objective fitness-for-duty and expertise measures in medicine and beyond.