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Tajmir, Shahein

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Tajmir

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Shahein

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Tajmir, Shahein
Author's Publications: search.filters.isAuthorOfPublication.77b9a590-a70c-4e26-a79d-6096602d6a19

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    Fully Automated Deep Learning System for Bone Age Assessment
    (Springer International Publishing, 2017) Lee, Hyunkwang; Tajmir, Shahein; Lee, Jenny; Zissen, Maurice; Yeshiwas, Bethel Ayele; Alkasab, Tarik; Choy, Garry; Do, Synho
    Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA. Our models use an ImageNet pretrained, fine-tuned convolutional neural network (CNN) to achieve 57.32 and 61.40% accuracies for the female and male cohorts on our held-out test images. Female test radiographs were assigned a BAA within 1 year 90.39% and within 2 years 98.11% of the time. Male test radiographs were assigned 94.18% within 1 year and 99.00% within 2 years. Using the input occlusion method, attention maps were created which reveal what features the trained model uses to perform BAA. These correspond to what human experts look at when manually performing BAA. Finally, the fully automated BAA system was deployed in the clinical environment as a decision supporting system for more accurate and efficient BAAs at much faster interpretation time (<2 s) than the conventional method.
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    Impact of Clinical Decision Support on Radiography for Acute Ankle Injuries: A Randomized Trial
    (Department of Emergency Medicine, University of California, Irvine School of Medicine, 2017) Tajmir, Shahein; Raja, Ali; Ip, Ivan; Andruchow, James; Silveira, Patricia; Smith, Stacy; Khorasani, Ramin
    Introduction: While only 15–20% of patients with foot and ankle injuries presenting to urgent care centers have clinically significant fractures, most undergo radiography. We examined the impact of electronic point-of-care clinical decision support (CDS) on adherence to the Ottawa Ankle Rules (OAR), as well as use and yield of foot and ankle radiographs in patients with acute ankle injury. Methods: We obtained institutional review board approval for this randomized controlled study performed April 18, 2012—December 15, 2013. All ordering providers credentialed at an urgent care affiliated with a quaternary care academic hospital were randomized to either receive or not receive CDS, based on the OAR and integrated into the physician order-entry system, with feedback at the time of imaging order. If the patient met OAR low-risk criteria, providers were advised against imaging and could either cancel the order or ignore the alert. We identified patients with foot and ankle complaints via ICD-9 billing codes and electronic health records and radiology reports reviewed for those who were eligible. Chi-square was used to compare adherence to the OAR (primary outcome), radiography utilization rate and radiography yield of foot and ankle imaging (secondary outcomes) between the intervention and control groups. Results: Of 14,642 patients seen at urgent care during the study period, 613 (4.2%, representing 632 visits) presented with acute ankle injury and were eligible for application of the OAR; 374 (59.2%) of these were seen by control-group providers. In the intervention group, CDS adherence was higher for both ankle (239/258=92.6% vs. 231/374=61.8%, p=0.02) and foot radiography (209/258=81.0% vs. 238/374=63.6%; p<0.01). However, ankle radiography use was higher in the intervention group (166/258=64.3% vs. 183/374=48.9%; p<0.01), while foot radiography use (141/258=54.6% vs. 202/374=54.0%; p=0.95) was not. Radiography yield was also higher in the intervention group (26/307=8.5% vs. 18/385=4.7%; p=0.04). Conclusion: Clinical decision support, previously demonstrated to improve guideline adherence for high-cost imaging, can also improve guideline adherence for radiography – as demonstrated by increased OAR adherence and increased imaging yield.