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: 8970690
• 负责人: JOANN G ELMORE
• 金额: $ 62.46万
• 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 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8970690
• 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; Computer Analysis; 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; digital media; distraction; eye hand coordination; health care delivery; imaging system; improved; innovation; innovative technologies; interest; medical schools; migration; new technology; next generation; novel; pedagogy; 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）：每年大约有 140 万女性依靠病理学家准确解读乳腺活检结果来诊断良性疾病或癌症。诊断错误非常频繁，并且可能导致患者治疗的改变，特别是在非典型增生和导管原位癌的阈值下，高达 50% 的病例被错误分类。这些错误背后的原因在很大程度上仍然未知。 类似于谷歌地图的技术现在可以对玻璃显微镜载玻片的高分辨率数字图像进行平移和缩放操作。这项技术实际上已经取代了医学院的显微镜，并迅速扩散到美国病理学实践中。没有研究评估病理学家对数字图像与载玻片的解读的准确性和效率。然而，这些“数字幻灯片”提供了一个新的机会来研究大量病理学家在操作和解释图像时的准确性、效率和观看行为。本申请中提出的创新分析技术与用于提高飞行员和空中交通管制员表现的技术类似。我们的具体目标是： 目标 1. 数字图像与载玻片：比较病理学家通过互联网查看数字化载玻片图像与在显微镜下查看原始载玻片的表现的解释准确性。病理学家的随机全国样本（N = 200）将分两个阶段以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用例和数字幻灯片、光标（即鼠标）跟踪数据和感兴趣区域（ROI）标记的诊断评估。这一目标的完成将为全玻片数字图像的比较诊断准确性建立基准。 目标 2. 解释性筛查行为：识别与诊断准确性和效率相关的视觉扫描模式。将在另外 60 名病理学家解释数字幻灯片以补充目标 1 的数据时收集详细的同步眼动追踪和光标追踪数据。将根据原始运动数据的计算机表示来分析观看模式。描述准确、高效的视觉扫描和光标移动的视频将成为教育下一代数字病理学家的宝贵工具。目标 3. 图像分析：检查和分类目标 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正确和错误的图像特征诊断。将确定诊断性和分散注意力的 ROI 的特征，将所有三个目标联系起来。总之，我们将确定数字化的全玻片图像在诊断上是否等同于原始载玻片。我们对病理学家确定的 ROI 的观察模式和特征进行深入的科学评估，对于理解诊断错误和分心来源至关重要。观察技术的优化将提高诊断性能，从而提高临床护理的质量。