A case of acute plastic deformation of the forearm in a medieval Hispano-Mudejar skeleton (13-14th Centuries AD)

9 p.Introduction: Acute plastic deformation refers to a traumatic bending or bowing without a detectable cortical defect.Case Presentation and Discussion: We describe a rare case from an individual that was exhumed from the HispanoMudejar necropolis in Uceda (Guadalajara, Spain) dated between the 13th and 14th centuries AD. The case corresponds to an adult woman, with a bowing involvement of the left ulna and radius. After making the differential diagnosis with various pathologies likely to present with this alteration, we reached the diagnosis of acute plastic deformation of the forearm through external and radiological examination and comparison with the healthy contralateral forearm. Conclusions: Acute plastic deformation is a rare traumatic injury, not described until the last century and only rarely described in palaeopathological contexts. We contribute a new case, the first being sufficiently documented, contributing to the knowledge and diagnosis of this type of trauma in the ancient bone, while deepening the knowledge of the living conditions of the medieval Mudejar population of Uceda.Banco de SantanderUniversidad Complutense de Madri

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

BACKGROUND: Majority of research and commercial efforts have focussed on use of artificial intelligence (AI) for fracture detection in adults, despite the greater long-term clinical and medicolegal implications of missed fractures in children. The objective of this study was to assess the available literature regarding diagnostic performance of AI tools for paediatric fracture assessment on imaging, and where available, how this compares with the performance of human readers. MATERIALS AND METHODS: MEDLINE, Embase and Cochrane Library databases were queried for studies published between 1 January 2011 and 2021 using terms related to 'fracture', 'artificial intelligence', 'imaging' and 'children'. Risk of bias was assessed using a modified QUADAS-2 tool. Descriptive statistics for diagnostic accuracies were collated. RESULTS: Nine eligible articles from 362 publications were included, with most (8/9) evaluating fracture detection on radiographs, with the elbow being the most common body part. Nearly all articles used data derived from a single institution, and used deep learning methodology with only a few (2/9) performing external validation. Accuracy rates generated by AI ranged from 88.8 to 97.9%. In two of the three articles where AI performance was compared to human readers, sensitivity rates for AI were marginally higher, but this was not statistically significant. CONCLUSIONS: Wide heterogeneity in the literature with limited information on algorithm performance on external datasets makes it difficult to understand how such tools may generalise to a wider paediatric population. Further research using a multicentric dataset with real-world evaluation would help to better understand the impact of these tools

Artificial intelligence for radiological paediatric fracture assessment: a systematic review.

BACKGROUND: Majority of research and commercial efforts have focussed on use of artificial intelligence (AI) for fracture detection in adults, despite the greater long-term clinical and medicolegal implications of missed fractures in children. The objective of this study was to assess the available literature regarding diagnostic performance of AI tools for paediatric fracture assessment on imaging, and where available, how this compares with the performance of human readers. MATERIALS AND METHODS: MEDLINE, Embase and Cochrane Library databases were queried for studies published between 1 January 2011 and 2021 using terms related to 'fracture', 'artificial intelligence', 'imaging' and 'children'. Risk of bias was assessed using a modified QUADAS-2 tool. Descriptive statistics for diagnostic accuracies were collated. RESULTS: Nine eligible articles from 362 publications were included, with most (8/9) evaluating fracture detection on radiographs, with the elbow being the most common body part. Nearly all articles used data derived from a single institution, and used deep learning methodology with only a few (2/9) performing external validation. Accuracy rates generated by AI ranged from 88.8 to 97.9%. In two of the three articles where AI performance was compared to human readers, sensitivity rates for AI were marginally higher, but this was not statistically significant. CONCLUSIONS: Wide heterogeneity in the literature with limited information on algorithm performance on external datasets makes it difficult to understand how such tools may generalise to a wider paediatric population. Further research using a multicentric dataset with real-world evaluation would help to better understand the impact of these tools

Computer-aided detection (CADx) for plastic deformation fractures in pediatric forearm

Osman, Onur (Arel Author)Bowing fractures are incomplete fractures of tubular long bones, often observed in pediatric patients, where plain radiographic film is the non-invasive imaging modality of choice in routine radiological workflow. Due to weak association between bent bone and distinct cortex disruption, bowing fractures may not be diagnosed properly while reading plain radiography. Missed fractures and dislocations are common in accidents and emergency practice, particularly in children. These missed injuries can result in more complicated treatment or even long-term disability. The most common reason for missed fractures is that junior radiologists or physicians lack expertise in pediatric skeletal injury diagnosis. Not only is additional radiation exposure inevitable in the case of misdiagnosis, but other consequences include the patient's prolonged uncomfortableness and possible unnecessary surgical procedures. Therefore, a computerized image analysis system, which would be secondary to the radiologists’ interpretations, may reduce adverse effects and improve the diagnostic rates of bowing fracture (detection and quantification). This system would be highly desirable and particularly useful in emergency rooms. To address this need, we investigated and developed a new Computer Aided Detection (CADx) system for pediatric bowing fractures. The proposed system has been tested on 226 cases of pediatric forearms with bowing fractures with respect to normal controls. Receiver operation characteristic (ROC) curves show that the sensitivity and selectivity of the developed CADx system are satisfactory and promising. A clinically feasible graphical user interface (GUI) was developed to serve the practical needs in the emergency room as a diagnostic reference. The developed CADx system also has strong potential to train radiology residents for diagnosing pediatric forearm bowing fractures

Computer-Aided Detection (Cadx) For Plastic Deformation Fractures In Pediatric Forearm

Bowing fractures are incomplete fractures of tubular long bones, often observed in pediatric patients, where plain radiographic film is the non-invasive imaging modality of choice in routine radiological workflow. Due to weak association between bent bone and distinct cortex disruption, bowing fractures may not be diagnosed properly while reading plain radiography. Missed fractures and dislocations are common in accidents and emergency practice, particularly in children. These missed injuries can result in more complicated treatment or even long-term disability. The most common reason for missed fractures is that junior radiologists or physicians lack expertise in pediatric skeletal injury diagnosis. Not only is additional radiation exposure inevitable in the case of misdiagnosis, but other consequences include the patient\u27s prolonged uncomfortableness and possible unnecessary surgical procedures. Therefore, a computerized image analysis system, which would be secondary to the radiologists’ interpretations, may reduce adverse effects and improve the diagnostic rates of bowing fracture (detection and quantification). This system would be highly desirable and particularly useful in emergency rooms. To address this need, we investigated and developed a new Computer Aided Detection (CADx) system for pediatric bowing fractures. The proposed system has been tested on 226 cases of pediatric forearms with bowing fractures with respect to normal controls. Receiver operation characteristic (ROC) curves show that the sensitivity and selectivity of the developed CADx system are satisfactory and promising. A clinically feasible graphical user interface (GUI) was developed to serve the practical needs in the emergency room as a diagnostic reference. The developed CADx system also has strong potential to train radiology residents for diagnosing pediatric forearm bowing fractures