Digital Pathology_Accuracy Viewing Behavior and Image Characterization

数字病理学_观看行为和图像表征的准确性

• 批准号: 8771432
• 负责人: JOANN G ELMORE
• 金额: $ 63.95万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-12-31 至 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8771432
• 关键词: Adoption; Air; Archives; Area; Attention; Atypical hyperplasia; Behavior; Benchmarking; Benign; Biopsy Specimen; Breast biopsy; Characteristics; Clinical; Color; Communities; Community Hospitals; Comparative Study; Complement; Complex; Computers; Data; Developing Countries; Diagnosis; Diagnostic; Diagnostic Errors; Diffuse; Disease; Ensure; Error Sources; Eye; Foundations; Glass; Goals; Gold; Health; Histopathology; Image; Image Analysis; Imaging Techniques; Imaging technology; Internet; Lead; Link; Machine Learning; Malignant Neoplasms; Maps; Measures; Medical; Medical Device; Medical Errors; Methods; Microscope; Microscopic; Movement; Mus; Noninfiltrating Intraductal Carcinoma; Pathologist; Pathology; Patients; Pattern; Performance; Phase; Physicians; Process; Randomized; Recommendation; Research; Research Personnel; Resolution; Risk; Rural; Rural Hospitals; Sampling; Scanning; Scientific Evaluation; Slide; Structure; System; Techniques; Technology; Testing; Texture; Time; Training; Visit; Visual; Woman; base; clinical care; comparative; computer monitor; diagnosis standard; diagnostic accuracy; digital; digital imaging; distraction; eye hand coordination; health care delivery; imaging system; improved; innovation; innovative technologies; interest; medical schools; migration; new technology; next generation; novel; sample fixation; screening; tool; trafficking; transmission process

DESCRIPTION (provided by applicant): Approximately 1.4 million women per year depend on pathologists to accurately interpret breast biopsies for a diagnosis of benign disease or cancer. Diagnostic errors are alarmingly frequent and likely lead to altered patient treatment, especially at the thresholds of atypical hyperplasia and ductal carcinoma in situ, where up to 50% of cases are misclassified. The causes underlying these errors remain largely unknown. Technology similar to Google Maps now allows pan and zoom manipulation of high-resolution digital images of glass microscope slides. This technology has virtually replaced the microscope in medical schools and is rapidly diffusing into U.S. pathology practices. No research has evaluated the accuracy and efficiency of pathologists' interpretion of digital images vs. glass slides. However, these "digital slides" offer a novel opportunity to study the accuracy, efficiency, and viewing behavior of a large number of pathologists as they manipulate and interpret images. The innovative analytic techniques proposed in this application are similar to those used to improve the performance of pilots and air traffic controllers. Our specific aims are: Aim 1. Digital Image vs. Glass Slides: To compare the interpretive accuracy of pathologists viewing digitized slide images over the Internet to their performance viewing original glass slides under a microscope. A randomized national sample of pathologists (N=200) will interpret 240 test cases in one or both formats in two phases. Measures will include a diagnostic assessment for each test case and for digital slides, cursor- (i.e., mouse) tracking data and region of interest (ROI) markings. Completion of this aim will establish benchmarks for the comparative diagnostic accuracy of whole-slide digital images. Aim 2. Interpretive Screening Behavior: To identify visual scanning patterns associated with diagnostic accuracy and efficiency. Detailed simultaneous eye-tracking and cursor-tracking data will be collected on 60 additional pathologists while they interpret digital slides to complement data from Aim 1. Viewing patterns will be analyzed from computer representations of raw movement data. Videos depicting accurate, efficient visual scanning and cursor movement will be valuable tools in educating the next generation of digital pathologists. Aim 3. Image Analyses: To examine and classify the image characteristics (including color, texture, and structure) of ROIs captured in Aims 1 and 2. Computer-based statistical learning techniques will be used to identify image characteristics that lead to correct and incorrect diagnoses. Characteristics of both diagnostic and distracting ROIs will be identified, linking all three aims. In summary, we will determine whether digitized whole-slide images are diagnostically equivalent to original glass slides. Our in-depth scientific evaluation of viewing patterns and characteristics of ROIs identified by pathologists will be critical to understanding diagnostic errors and sources of distraction. Optimization of viewing techniques will improve diagnostic performance and thus, the quality of clinical care.

描述（由申请人提供）：每年约有140万女性取决于病理学家，以准确解释乳房活检以诊断出良性疾病或癌症。诊断错误频繁频繁，可能导致患者治疗的改变，尤其是在非典型增生和导管癌的阈值下，在那里，多达50％的病例被错误分类。这些错误的原因仍然很大程度上是未知的。 类似于Google Maps的技术现在允许对玻璃显微镜幻灯片的高分辨率数字图像进行平底锅操纵。该技术实际上取代了医学院的显微镜，并正在迅速扩散到美国病理学实践中。没有研究评估了病理学家对数字图像与载玻片的解释的准确性和效率。但是，这些“数字幻灯片”为研究大量病理学家操纵和解释图像时的准确性，效率和观看行为提供了新的机会。本应用程序中提出的创新分析技术类似于用于提高飞行员和空中交通管制员性能的创新分析技术。我们的具体目的是：目标1。数字图像与载玻片：比较互联网上查看数字化幻灯片图像的病理学家的解释准确性与显微镜下的性能观看原始载玻片的性能。一个随机的民族病理学家样本（n = 200）将以两个阶段的一种或两种格式解释240个测试用例。措施将包括对每个测试案例的诊断评估以及数字幻灯片，光标 - （即鼠标）跟踪数据和感兴趣区域（ROI）标记。该目标的完成将建立基准，以便全扫描数字图像的比较诊断准确性。 目标2。解释性筛查行为：确定与诊断准确性和效率相关的视觉扫描模式。详细的同时眼球跟踪和光标跟踪数据将在60位其他病理学家中收集，同时将数字幻灯片解释以补充AIM 1的数据。将从原始移动数据的计算机表示中分析查看模式。描述准确，高效的视觉扫描和光标运动的视频将是教育下一代数字病理学家的宝贵工具。目标3。图像分析：在AIMS 1和2中捕获的ROI的图像特征（包括颜色，纹理和结构）。基于计算机的统计学习技术将用于识别导致校正和不正确诊断的图像特征。将确定诊断和分心的ROI的特征，并将所有三个目标联系起来。总而言之，我们将确定数字化的全扫描图像是否在诊断上等同于原始载玻片。我们对病理学家确定的ROI的观看模式和特征的深入科学评估对于理解诊断错误和分心的来源至关重要。观看技术的优化将改善诊断性能，从而改善临床护理的质量。