RESUMO
Purpose: Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient's medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction. Materials and methods: An algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from the brain tumor segmentations. Results: UNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study. Conclusion: Integration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.
RESUMO
Introduction: In controversial fashion, the presence of an enlarged external occipital protuberance has been recently linked to excessive use of handheld electronic devices. We sought to determine the prevalence of this protuberance in a diverse age group of adults from two separate time periods, before and approximately 10 years after the release of the iPhone, to further characterize this theory, as if indeed valid, such a relationship could direct preventative behavior. Materials and Methods: Eighty-two cervical spine radiographs between March 7, 2007 through June 29, 2007 and 147 cervical spine radiographs between October 25, 2017 through January 1, 2018 were reviewed for the presence or absence of an exophytic external occipital protuberance. Influence of sex and age were also assessed. Results: There were 41/82 (50%) patients within the 2007 pre-iPhone group with an exophytic external occipital protuberance, ranging from 2.7-33.8 mm in length. Twenty-seven out of 82 (32.9%) had an external occipital protuberance at or above 10 mm. There were 49/147 (33.3%) patients within the 2017 post-iPhone group with an exophytic external occipital protuberance, ranging from 4.4-53.8 mm in length. Thirty-three out of 147 (22.4%) had an external occipital protuberance at or above 10 mm. When considering accessibility to the iPhone, sex, and age to the presence of an exophytic external occipital protuberance, only sex has a statistically significant association, p=0.000000033. Conclusion: We found no significant association with iPhone accessibility and an exophytic external occipital protuberance. Due to inherent limitations in the retrospective nature of the study, future research is needed to better examine the association of handheld electronic devices with exophytic external occipital protuberances.
