Automatic Diagnosis of Fracture using Deep Learning and External Validation: A Systematic Review and Meta-Analysis
Keywords:
CNN, deep learning, External Validation, fractureAbstract
Deep learning is a hot area for automatically diagnosing X-rays for bone fractures. Scientists are constantly working on improving clinical practices by exploring new methods. Identifying the fracture, especially hidden, using an automated method is still challenging. Very less external validation on the already studied method is available. This systematic review investigates where current artificial intelligence research stands in assisting radiologists with the correct diagnosis of bone fracture and what are the future directions.
The hybrid approach model is necessary for images collected from the ImageNet Dataset or the Hospital's Radiology department. The processing using the variants of CNN helps in acquiring adequate accuracy for detecting the fracture in the bone in the X-ray image. X-Ray images have been taken, and the output is compared with the pre-trained dataset from ImageNet like InceptionV3, Resnet50, and VGG16.
A systematic review was performed on PubMed and Google Scholar for the studies published between 2019 and 2023. We have included ten articles for the study. These articles are thoroughly analysed and compared for factors like Accuracy, dataset, bone types, etc. External validation is also analysed for each study.
Research in detecting bone fractures through deep learning is continuously increasing. The deep learning model is a good aid in assisting radiologists and clinicians in detecting fractures. Various studies have been performed on bones, but most still need more external validation and heterogeneous data.
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