Enhancing the Diagnostic Accuracy of Sacroiliitis: A Machine Learning Approach Applied to Computed Tomography Imaging.

Fu, Qingsong; Yuan, Xueru; Han, Xinyou; Wang, Weibin; Zhang, Jiakai; Yuan, Xinhua

Fu, Qingsong; Yuan, Xueru; Han, Xinyou; Wang, Weibin; Zhang, Jiakai; Yuan, Xinhua.

Afiliación

Fu Q; Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China.
Yuan X; Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo, Zhejiang, China.
Han X; Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China.
Wang W; Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China.
Zhang J; Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China.
Yuan X; Department of Orthopaedics, Ningbo No. 2 Hospital, Ningbo, Zhejiang, China.

Br J Hosp Med (Lond) ; 85(8): 1-13, 2024 Aug 30.

Article en En | MEDLINE | ID: mdl-39212564

ABSTRACT

ABSTRACT

Aims/Background Sacroiliitis is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies. This study aims to improve the diagnostic accuracy of sacroiliitis by applying advanced machine learning techniques to computed tomography (CT) images. Methods We employed five convolutional neural network (CNN) models-Visual Geometry Group 16-layer Network (VGG16), ResNet101, DenseNet, Inception-v4, and ResNeXt-50-to analyze a dataset of 830 CT images, including both sacroiliitis and non-sacroiliitis cases. Each model's performance was evaluated using metrics such as accuracy, precision, recall, F1 score, Receiver Operating Characteristic (ROC), and Area Under the Curve (AUC). The interpretability of the models' decisions was enhanced using Gradient-weighted Class Activation Mapping (Grad-CAM) visualization. Results The ResNeXt-50 and Inception-v4 models demonstrated superior performance, achieving the highest accuracy and F1 scores among the tested models. Grad-CAM visualizations offered insights into the decision-making processes, highlighting the models' focus on relevant anatomical features critical for accurate diagnosis. Conclusion The use of CNN models, particularly ResNeXt-50 and Inception-v4, significantly improves the diagnosis of sacroiliitis from CT images. These models not only provide high diagnostic accuracy but also offer transparency in their decision-making processes, aiding clinicians in understanding and trusting Artificial Intelligence (AI)-driven diagnostics.

Asunto(s)

Aprendizaje Automático; Sacroileítis; Tomografía Computarizada por Rayos X; Humanos; Sacroileítis/diagnóstico por imagen; Tomografía Computarizada por Rayos X/métodos; Redes Neurales de la Computación; Masculino; Femenino; Adulto; Persona de Mediana Edad; Curva ROC

Palabras clave

CT image recognition; Grad-CAM visualization; convolutional neural network (CNN); machine learning; sacroiliitis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Sacroileítis / Aprendizaje Automático Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: Br J Hosp Med (Lond) Asunto de la revista: HOSPITAIS / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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