Next Article in Journal
Rapid Decline of IFN-γ Spot-Forming Cells in Pleural Lymphocytes during Treatment in a Patient with Suspected Tuberculosis Pleurisy
Previous Article in Journal
Clinical Importance of Drug Adherence during Tyrosine Kinase Inhibitor Therapy for Chronic Myelogenous Leukemia in Chronic Phase
Open AccessArticle

Machine Learning Models for Abnormality Detection in Musculoskeletal Radiographs

Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
Reports 2019, 2(4), 26; https://doi.org/10.3390/reports2040026
Received: 5 October 2019 / Revised: 17 October 2019 / Accepted: 20 October 2019 / Published: 22 October 2019
Increasing radiologist workloads and increasing primary care radiology services make it relevant to explore the use of artificial intelligence (AI) and particularly deep learning to provide diagnostic assistance to radiologists and primary care physicians in improving the quality of patient care. This study investigates new model architectures and deep transfer learning to improve the performance in detecting abnormalities of upper extremities while training with limited data. DenseNet-169, DenseNet-201, and InceptionResNetV2 deep learning models were implemented and evaluated on the humerus and finger radiographs from MURA, a large public dataset of musculoskeletal radiographs. These architectures were selected because of their high recognition accuracy in a benchmark study. The DenseNet-201 and InceptionResNetV2 models, employing deep transfer learning to optimize training on limited data, detected abnormalities in the humerus radiographs with 95% CI accuracies of 83–92% and high sensitivities greater than 0.9, allowing for these models to serve as useful initial screening tools to prioritize studies for expedited review. The performance in the case of finger radiographs was not as promising, possibly due to the limitations of large inter-radiologist variation. It is suggested that the causes of this variation be further explored using machine learning approaches, which may lead to appropriate remediation. View Full-Text
Keywords: machine learning; convolutional neural network; transfer learning; abnormality detection; musculoskeletal radiographs machine learning; convolutional neural network; transfer learning; abnormality detection; musculoskeletal radiographs
Show Figures

Figure 1

MDPI and ACS Style

Chada, G. Machine Learning Models for Abnormality Detection in Musculoskeletal Radiographs. Reports 2019, 2, 26.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop