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Assessment of the accuracy of lung lesions diagnosis in adolescents with osteosarcoma using artificial intelligence

0Citations signalées, ce qui n’est pas une note de qualité
2Institutions déclarées
1Pays d’affiliation déclarés

Rattachement africain : ru. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

ABSTRACT Background Lung metastases in osteosarcoma (OS) are the main cause of the death. The accuracy of the diagnosis of nodules by computed tomography (CT) of the lungs is critically important for determining the disseminated stage of the disease and planning surgical treatment. The use of artificial intelligence (AI) in the search for lung nodules increases the accuracy of diagnosis and reduces the chance of missing metastases. Objective to evaluate the accuracy of lung nodules diagnosis in adolescents with OS using AI. Methods A retrospective assessment of CT scans of adolescents with OS was performed. A pathological nodule with an average size of ≥4 mm was considered a target finding. The diagnostic accuracy of an AI algorithm previously trained on an adult dataset was evaluated, and the number of false positives (FP) and false negatives (FN) was determined. Sensitivity, specificity, accuracy, area under the ROC curve (AUC), positive predictive value, negative predictive value, and F1- measure were calculated. Based on the obtained results, the effectiveness of the algorithm was assessed. Results 248 CT scans of adolescents with OS were evaluated. The following results were obtained: in 5 cases, the AI algorithm showed a FP result (2%), in 34 cases, it showed a FN result (13.7%), and in 209 cases, a correct result (both true positive and true negative) (84.3%). The diagnostic accuracy of the algorithm was 0.843 (95% CI 0.794-0.887). The application of the AI algorithm in the practice of an X-ray doctor in a specific clinical task would allow to increase the sensitivity from 0.805 to 0.891, while ensuring an absolute decrease in the number of FN results by 8.6% and a relative decrease by 44%. Conclusion The obtained results confirm the practical value of the application of the AI algorithm and justify the implementation of AI-assisted systems in the diagnostic protocols for lung metastases in adolescents with OS.

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Assessment of the accuracy of lung lesions diagnosis in adolescents with osteosarcoma using artificial intelligence
Date Crossref
10/06/2026
Éditeur
openRxiv
Type
posted-content

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Les institutions déclarées

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Radiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and EducationLung Cancer Diagnosis and Treatment

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