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Deep learning of birth-related infant clavicle fractures: a potential virtual consultant for fracture dating
Andy Tsai1, P Ellen Grant2,3, Simon K Warfield2
1Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA. andy.tsai@childrens.harvard.edu.
Insights
A new deep learning model accurately dates infant clavicle fractures using radiographs. This tool aids in establishing timelines for suspected infant abuse cases.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pediatric Radiology
Background:
- Accurate dating of infant skeletal injuries from radiographs is crucial for abuse investigations.
- Birth-related clavicle fractures in infants are used as a surrogate for dating abuse-related fractures.
- Existing methods lack precision in establishing timelines for traumatic skeletal events.
Purpose of the Study:
- To develop and train a deep learning algorithm for precise dating of infant birth-related clavicle fractures.
- To enhance the accuracy of radiographic age estimation for infant fractures.
- To provide a tool for objective assessment in forensic pediatric radiology.
Main Methods:
- A deep learning model, adapted from facial age estimation, was utilized.
- A database of 416 infant clavicle radiographs (infants ≤ 3 months) was curated.
- The model was trained, validated, and tested using a four-fold cross-validation procedure.
Main Results:
- The deep learning model achieved a mean absolute error of 4.2 days and a root mean square error of 6.3 days.
- The intraclass correlation coefficient (ICC) was 0.919, indicating high reliability.
- Approximately 83.7% of fracture age estimates were accurate within 7 days of the ground truth.
Conclusions:
- The deep learning model shows promising results for radiographic dating of infant clavicle fractures.
- Further development and validation could establish this model as a valuable tool for radiologists.
- This technology may assist in objectively assessing fracture ages in suspected infant abuse investigations.
Background:
In infant abuse investigations, dating of skeletal injuries from radiographs is desirable to reach a clear timeline of traumatic events. Prior studies have used infant birth-related clavicle fractures as a surrogate to develop a framework for dating of abuse-related fractures.
Objective:
To develop and train a deep learning algorithm that can accurately date infant birth-related clavicle fractures.
Materials And Methods:
We modified a deep learning model initially designed for face-age estimation to date infant clavicle fractures. We conducted a computerized search of imaging reports and other medical records at a tertiary children's hospital to identify radiographs of birth-related clavicle fracture in infants ≤ 3 months old (July 2003 to March 2021). We used the resultant database for model training, validation and testing. We evaluated the performance of the deep learning model via a four-fold cross-validation procedure, and calculated accuracy metrics: mean absolute error (MAE), root mean square error (RMSE), intraclass correlation coefficient (ICC) and cumulative score.
Results:
The curated database consisted of 416 clavicle radiographs from 213 infants. Average chronological age (equivalent to fracture age) at time of imaging was 24 days. This model estimated the ages of the clavicle fractures with MAE of 4.2 days, RMSE of 6.3 days and ICC of 0.919. On average, 83.7% of the fracture age estimates were accurate to within 7 days of the ground truth.
Conclusion:
Our deep learning study provides encouraging results for radiographic dating of infant clavicle fractures. With further development and validation, this model might serve as a virtual consultant to radiologists estimating fracture ages in cases of suspected infant abuse.
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