Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is needed for diagnosis. Recent advances in the field of Deep Learning (DL), a subfield of Artificial Intelligence (AI), have shown that wrist fracture detection can be automated using Convolutional Neural Networks. However, previous studies did not pay close attention to the difficult cases which can only be confirmed via CT imaging. In this study, we have developed and analyzed a state-of-the-art DL-based pipeline for wrist (distal radius) fracture detection—DeepWrist, and evaluated it against one general population test set, and one challenging test set comprising only cases requiring confirmation by CT. Our results reveal that a typical state-of-the-art approach, such as DeepWrist, while having a near-perfect performance on the general independent test set, has a substantially lower performance on the challenging test set—average precision of 0.99 (0.99–0.99) versus 0.64 (0.46–0.83), respectively. Similarly, the area under the ROC curve was of 0.99 (0.98–0.99) versus 0.84 (0.72–0.93), respectively. Our findings highlight the importance of a meticulous analysis of DL-based models before clinical use, and unearth the need for more challenging settings for testing medical AI systems.
This paper tackles the problem of developing active learning (AL) methods in the context of knee osteoarthritis (OA) diagnosis from X-ray images. OA is known to be a huge burden for society, and its associated costs are constantly rising. Automatic diagnostic methods can potentially reduce these costs, and Deep Learning (DL) methodology may be its key enabler. To date, there have been numerous studies on knee OA severity grading using DL, and all but one of them assume a large annotated dataset available for model development. In contrast, our study shows one can develop a knee OA severity grading model using AL from as little as 50 samples randomly chosen from a pool of unlabeled data. The main insight of this work is that the performance of AL improves when the model developer leverages the consistency regularization technique, commonly applied in semi-supervised learning.
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