LERA- Lower Extremity RAdiographs
Dataset Description
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 2003 and 2014. The dataset consists of images of the foot, knee, ankle, or hip associated with each patient.
Canonical URL
https://aimi.stanford.edu/lera-lower-extremity-radiographs
Full Description
Musculoskeletal disorders (MSDs), which encompass a wide variety of bone, soft tissue, and joint abnormalities, are a major healthcare challenge around the world. MSDs are typically diagnosed using radiographs; however, variations in diagnostic interpretation quality can often lead to diagnostic errors. 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.
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. To aid computational models in accurately identifying diverse abnormalities in highly-variable radiographs of multiple body parts, we are releasing LERA (Lower Extremity RAdiographs). 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 [citation after publication].
Dataset Details
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 2003 and 2014. The dataset consists of images of the foot, knee, ankle, or hip associated with each patient.
Assignment of Labels
Our dataset includes a .csv file matching patient identification numbers to diagnosis labels and radiograph types. Diagnosis labels were assigned as follows. 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). 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). 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. As a result, we are confident that dataset is highly accurate and will serve a suitable resource for testing deep learning models.
Additional Information
Please note that all labels are assigned at the patient level, indicating that the same classification applies to all images for a particular patient. 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.
This study was supported by the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). The research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under Award Number R01LM012966 and Stanford Child Health Research Institute (Stanford NIH-NCATS-CTSA Grant #UL1 TR001085). This research used data or services provided by STARR, "STAnford medicine Research data Repository,” a clinical data warehouse made possible by the Stanford School of Medicine Research Office.
Terms & Conditions
Stanford University School of Medicine LERA- Lower Extremity RAdiographs Dataset Research Use Agreement
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