Evidence map›Paper›PMID 39568996›Full record

ArticleFrontiers in microbiology2024

A transformer-based deep learning model for identifying the occurrence of acute hematogenous osteomyelitis and predicting blood culture results.

Yingtu Xia, Qiang Kang, Yi Gao, Jiuhui Su

Abstract read
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Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yingtu XiaDepartment of Orthopedics, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Qiang KangDepartment of Radiology, Xing'an League People's Hospital of Inner Mongolia, Ulanhot, Inner Mongolia, China.
Yi GaoDepartment of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Jiuhui SuDepartment of Orthopaedics, Haicheng Bonesetting Hospital, Haicheng, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

anti-infection treatmentblood culturedeep learningosteomyelitispathogenic microorganism

Identifiers

PMID39568996
PMCPMC11578118

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.