[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122421-en":3,"doc-seo-122421-105":29,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122421,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Machine Learning as a Tool in Detecting Rib Fractures in Pediatric Patients","Rib fractures are among the most common and distinctive findings associated with child abuse, yet they can be subtle and challenging to recognize even for experienced radiologists. An investigation developed a machine learning algorithm to determine the presence or absence of rib fractures on chest radiographs in pediatric patients under 3 years old. Radiology archive reports from Jan 2020 to Jun 2022 were used to construct datasets labeled for no fracture versus presence of fracture, and models were trained with Histogram of Oriented Gradients (HOG) features. The resulting trained model achieved 95.9% overall accuracy with strong sensitivity and specificity.","Machine Learning as a Tool in Detecting Rib Fractures in Pediatric Patients  \nVamsish Satoor1 , Megan B Marine2  \n1 Indiana University School of Medicine; 2 Indiana University School of Medicine, Department  \nof Radiology & Imaging Sciences  \nBackground/Objective:  \nRib fractures are one of the most specific fractures in child abuse and are among the most common identified, reported in up to 45% of cases. Given rib fractures can be subtle and difficult for even experienced radiologists to identify, a diagnostic tool to improve the detection accuracy would provide value in evaluation of child abuse.The objective of this investigation is to create a machine learning algorithm with the ability to recognize the presence or absence of rib fractures on chest radiographs in pediatric patients less than 3 years old.  \nMethods:  \nThe IU Health radiology archive (DORIS) was searched for reports (Jan 2020-June 2022) for skeletal surveys in patients less than 3 years of age. 3 view chest radiographs (frontal and bilateral oblique) from the surveys were fit into two datasets: no rib fracture or presence of rib fracture. A machine learning model was trained and tested using the constructed datasets with Histogram of Oriented Gradients (HOG) features extracted to refine the prediction accuracy.  \nResults:  \nThe study group contained 100 patients (40 females , mean age 8 months) with 299 radiographs with reported rib fractures. The gender and age-matched control group included 100 patients had 300 radiographs without reported rib fractures. The overall performance accuracy of the trained model was 95.9% . PPV, NPV, sensitivity, and specificity were 96.83%, 88.32%, 87.32%, and 97.09% respectively.  \nPotential Impact:  \nGiven the demonstrated effectiveness of the machine learning model, it could serve as an aid to be used in the interpretation of skeletal surveys for child abuse. More importantly however, it may also be considered as a screening tool in identifying rib fractures in unsuspected patients, such as chest radiographs in the emergency room setting where ribs may not be the primary focus of evaluation and fractures may go overlooked.","cbCaiohpKYvIqpzG","https://ap.wps.com/l/cbCaiohpKYvIqpzG","pdf",100933,1,"English","en",105,"# Background and Objective\n# Methods\n# Results\n# Potential Impact","[{\"question\":\"What problem does the study address in pediatric imaging?\",\"answer\":\"Rib fractures in young children can be difficult to detect reliably on chest radiographs, even for experienced radiologists, which affects evaluations related to child abuse.\"},{\"question\":\"How were the datasets for the machine learning model created?\",\"answer\":\"Radiology archive reports (Jan 2020–Jun 2022) for skeletal surveys in children under 3 years were searched, and 3-view chest radiographs were grouped into two datasets: no rib fracture and presence of rib fracture.\"},{\"question\":\"What performance did the trained model achieve?\",\"answer\":\"The trained model reached 95.9% overall accuracy, with PPV 96.83%, NPV 88.32%, sensitivity 87.32%, and specificity 97.09%.\"},{\"question\":\"How could the model be used in clinical practice?\",\"answer\":\"It could support interpretation of skeletal surveys for child abuse and may also function as a screening aid in settings like emergency rooms where rib fractures are not the primary focus.\"}]","Machine Learning as a Tool in Detecting Rib Fractures in Pediatric Patients | 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problem does the study address in pediatric imaging?","Question",{"text":73,"@type":74},"Rib fractures in young children can be difficult to detect reliably on chest radiographs, even for experienced radiologists, which affects evaluations related to child abuse.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How were the datasets for the machine learning model created?",{"text":78,"@type":74},"Radiology archive reports (Jan 2020–Jun 2022) for skeletal surveys in children under 3 years were searched, and 3-view chest radiographs were grouped into two datasets: no rib fracture and presence of rib fracture.",{"name":80,"@type":71,"acceptedAnswer":81},"What performance did the trained model achieve?",{"text":82,"@type":74},"The trained model reached 95.9% overall accuracy, with PPV 96.83%, NPV 88.32%, sensitivity 87.32%, and specificity 97.09%.",{"name":84,"@type":71,"acceptedAnswer":85},"How could the model be used in clinical practice?",{"text":86,"@type":74},"It could support 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