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A transformer-based deep learning model for identifying the occurrence of acute hematogenous osteomyelitis and
Yingtu Xia1, Qiang Kang2, Yi Gao3
1Department of Orthopedics, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Insights
Easily accessible laboratory indicators can help diagnose pediatric osteomyelitis and predict bacterial culture success. This simplifies diagnosis and guides antibiotic treatment decisions for children.
Area of Science:
- Pediatric Medicine
- Medical Diagnostics
- Artificial Intelligence in Healthcare
Background:
- Acute hematogenous osteomyelitis is the most common bone infection in children, with increasing incidence.
- Pediatric osteomyelitis diagnosis is challenging, often requiring advanced imaging.
- Bacterial culture results significantly impact treatment strategies for diagnosed patients.
Purpose of the Study:
- To analyze correlations between laboratory indicators and pediatric osteomyelitis.
- To develop a deep learning model for identifying osteomyelitis in children.
- To predict bacterial culture success and hospital stay duration using laboratory data.
Main Methods:
- A Transformer-based deep learning model was developed using data from 634 pediatric patients.
- Correlations between laboratory indicators, osteomyelitis diagnosis, and confused diagnoses were analyzed.
- The model was trained to predict osteomyelitis, hospital stay length, and bacterial culture outcomes.
Main Results:
- Laboratory indicators correlate with acute hematogenous osteomyelitis presence, enabling effective identification via a deep learning model.
- Laboratory indicators partially predict hospital stay duration for osteomyelitis patients.
- A deep learning model successfully predicts bacterial culture outcomes, even when direct correlations are weak.
Conclusions:
- Accessible laboratory indicators can diagnose pediatric osteomyelitis and predict bacterial culture success.
- These findings simplify diagnosis and inform empirical antibiotic therapy decisions.
- The deep learning model offers a valuable tool for managing pediatric osteomyelitis.
Background:
Acute hematogenous osteomyelitis is the most common form of osteomyelitis in children. In recent years, the incidence of osteomyelitis has been steadily increasing. For pediatric patients, clearly describing their symptoms can be quite challenging, which often necessitates the use of complex diagnostic methods, such as radiology. For those who have been diagnosed, the ability to culture the pathogenic bacteria significantly affects their treatment plan.
Method:
A total of 634 patients under the age of 18 were included, and the correlation between laboratory indicators and osteomyelitis, as well as several diagnoses often confused with osteomyelitis, was analyzed. Based on this, a Transformer-based deep learning model was developed to identify osteomyelitis patients. Subsequently, the correlation between laboratory indicators and the length of hospital stay for osteomyelitis patients was examined. Finally, the correlation between the successful cultivation of pathogenic bacteria and laboratory indicators in osteomyelitis patients was analyzed, and a deep learning model was established for prediction.
Result:
The laboratory indicators of patients are correlated with the presence of acute hematogenous osteomyelitis, and the deep learning model developed based on this correlation can effectively identify patients with acute hematogenous osteomyelitis. The laboratory indicators of patients with acute hematogenous osteomyelitis can partially reflect their length of hospital stay. Although most laboratory indicators lack a direct correlation with the ability to culture pathogenic bacteria in patients with acute hematogenous osteomyelitis, our model can still predict whether the bacteria can be successfully cultured.
Conclusion:
Laboratory indicators, as easily accessible medical information, can identify osteomyelitis in pediatric patients. They can also predict whether pathogenic bacteria can be successfully cultured, regardless of whether the patient has received antibiotics beforehand. This not only simplifies the diagnostic process for pediatricians but also provides a basis for deciding whether to use empirical antibiotic therapy or discontinue treatment for blood cultures.
