Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning Classification
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, im...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Biology |
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| Online Access: | https://www.mdpi.com/2079-7737/11/5/749 |
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| author | Kelsey M. Kyllonen Keith L. Monson Michael A. Smith |
| author_facet | Kelsey M. Kyllonen Keith L. Monson Michael A. Smith |
| author_sort | Kelsey M. Kyllonen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:18:41Z |
| format | Article |
| id | doaj.art-b101ccb78d044e5db5da502aaa04eeea |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-10T03:18:41Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-b101ccb78d044e5db5da502aaa04eeea2023-11-23T10:07:55ZengMDPI AGBiology2079-77372022-05-0111574910.3390/biology11050749Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning ClassificationKelsey M. Kyllonen0Keith L. Monson1Michael A. Smith2FBI Laboratory, Quantico, VA 22135, USAFBI Laboratory, Quantico, VA 22135, USAFBI Laboratory, Quantico, VA 22135, USAA 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.https://www.mdpi.com/2079-7737/11/5/749forensic anthropologychildrenfracture datinghealing stageradiographsmachine learning |
| spellingShingle | Kelsey M. Kyllonen Keith L. Monson Michael A. Smith Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning Classification Biology forensic anthropology children fracture dating healing stage radiographs machine learning |
| title | Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning Classification |
| title_full | Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning Classification |
| title_fullStr | Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning Classification |
| title_full_unstemmed | Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning Classification |
| title_short | Postmortem and Antemortem Forensic Assessment of Pediatric Fracture Healing from Radiographs and Machine Learning Classification |
| title_sort | postmortem and antemortem forensic assessment of pediatric fracture healing from radiographs and machine learning classification |
| topic | forensic anthropology children fracture dating healing stage radiographs machine learning |
| url | https://www.mdpi.com/2079-7737/11/5/749 |
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