This study was supported by the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). Also, since images were collected over a twelve-year period, the dataset includes highly variable images, with radiographs in varying in size, resolution, and color there may also be duplicate images. Please note that all labels are assigned at the patient level, indicating that the same classification applies to all images for a particular patient. As a result, we are confident that dataset is highly accurate and will serve a suitable resource for testing deep learning models. Due to these loose constraints as well as the fact that ground truth in radiographic examinations can be difficult to establish, we predicted that the dataset contained a small percentage of incorrect labels in order to correct for this, two board-certified radiologists, each with 6 years of post-graduate experience, independently labeled the images in this dataset through majority vote consensus between the two radiologists and the prospective exam report. The designation of a radiograph as normal refers to the attending radiologist’s interpretation of a radiograph as normal given the age of the patient all radiographs that fall outside this categorization are designated as abnormal (which may be as varied as degeneration, hardware, arthritis, and fractures, among others). After prospective evaluation of all radiographs associated with a patient, the attending radiologist at the time of initial interpretation assigned each patient a binary classification of normal (y=0) or abnormal (y=1). Diagnosis labels were assigned as follows. csv file matching patient identification numbers to diagnosis labels and radiograph types. The dataset consists of images of the foot, knee, ankle, or hip associated with each patient. In this retrospective, HIPAA-compliant, IRB-approved study, we collected data from 182 patients who underwent a radiographic examination at the Stanford University Medical Center between 20. This dataset was used as the held-out test set in our recent study, which found that a single pre-trained CNN was effective in performing generalized abnormality detection in lower extremities. To aid computational models in accurately identifying diverse abnormalities in highly-variable radiographs of multiple body parts, we are releasing LERA (Lower Extremity RAdiographs). The recent revolution in deep learning techniques for image analysis suggests that convolutional neural networks (CNNs) can serve as an effective tool for computer-aided detection of radiograph abnormalities. This problem is often compounded by a lack of available tools to triage large volumes of unread examinations, which can result in numerous adverse downstream effects related to delay of diagnosis and treatment. MSDs are typically diagnosed using radiographs however, variations in diagnostic interpretation quality can often lead to diagnostic errors. Musculoskeletal disorders (MSDs), which encompass a wide variety of bone, soft tissue, and joint abnormalities, are a major healthcare challenge around the world. Any duplication or distribution of the information contained herein is strictly prohibited.In this retrospective, HIPAA-compliant, IRB-approved study, we collected data from 182 patients who underwent a radiographic examination at the Stanford University Medical Center between 20. No warranty of any kind, either expressed or implied, is made as to the accuracy, reliability, timeliness, or correctness of any translations made by a third-party service of the information provided herein into any other language. Links to other sites are provided for information only – they do not constitute endorsements of those other sites. A licensed medical professional should be consulted for diagnosis and treatment of any and all medical conditions. ![]() ![]() The information provided herein should not be used during any medical emergency or for the diagnosis or treatment of any medical condition. This site complies with the HONcode standard for trustworthy health information: verify here. Learn more about A.D.A.M.'s editorial policy editorial process and privacy policy. is among the first to achieve this important distinction for online health information and services. ![]() follows rigorous standards of quality and accountability. is accredited by URAC, for Health Content Provider (URAC's accreditation program is an independent audit to verify that A.D.A.M.
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