Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning Classification.

Abstract

Simple SummaryBeing able to estimate from X-rays alone how long ago a child’s bone was fractured is important for prosecuting suspected child abuse of living or dead children. This estimate can also help identify a child when all that remains are bones. Experts use various indicators to make these estimates of the age of healing and fully healed fractures, in living and deceased persons, even years after the injury occurred. However, it is not a precise science. We proposed a method using a new combination of indicators to classify fracture healing in children and adolescents. We tested its accuracy with a public database of X-rays of children’s fractures taken during the treatment and healing process. We used part of the X-ray database for training artificial intelligence (AI, or machine learning) programs to classify stages of bone healing when using our new system. We used another portion of the same database to test the performance of the AI system that had been trained with our new classification system. Our new system addresses certain classification ambiguities of a currently used system and is similar in accuracy.A timeline of pediatric bone healing using fracture healing characteristics that can be assessed solely using radiographs would be practical for forensic casework, where the fracture event may precede death by days, months, or years. However, the dating of fractures from radiographs is difficult, imprecise, and lacks consensus, as only a few aspects of the healing process are visible on radiographs. Multiple studies in both the clinical and forensic literature have attempted to develop a usable scale to assess pediatric bone healing on radiographs using various healing characteristics. In contrast to the orthopedic definition, a fracture in forensic casework is only considered to be healed when the area around the fracture has been remodeled to the point that the fracture is difficult to detect on a radiograph or on the surface of the bone itself, a process that can take several years. We subjectively assessed visible characteristics of healing in radiograms of fractures occurring in 942 living children and adolescents. By dividing these assessments into learning and test (validation) sets, the accuracy of a newly proposed fracture healing scale was compared to a previous study. Two machine learning models were used to test predictions of the new scale. All three models produced similar estimates with substantial imprecision. Results corroborate the Malone model with an independent dataset and support the efficacy of using less complex models to estimate fracture age in children.