Efficient pulmonary nodules classification using radiomics and different artificial intelligence strategies

Sadgal, Mohammed; Raafat, M.; Yehia, Sherif; Khalil, Magdy M.

doi:10.1186/s13244-023-01441-6

Cited by 10 publications

(1 citation statement)

References 16 publications

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“…Patches meeting the PJI diagnostic criteria were selected and utilized to train a supervised learning model based on ResNet34, resulting in an intelligent model for PJI image-level diagnosis [25]. The aforementioned ResNet model has been widely applied for the intelligent recognition of images and pathological pictures [39], yielding high accuracy in the pathological diagnosis of malignancies such as lung cancer, gastric cancer, breast cancer, and bladder cancer [40][41][42][43]. Despite achieving an accuracy of 93.22% and a recall of 96.49% in pathology image-level diagnoses of PJI images during external testing, at the patient level, misidenti cation of pathological images led to errors and omissions in diagnosing prosthetic joint infections (with an AUC of 0.81) [25].…”

Section: The Application Of Arti Cial Intelligence In Image Recogniti...mentioning

confidence: 99%

Clinically applicable optimized periprosthetic joint infection diagnosis via AI-based pathology

Ni,

Tao,

et al. 2024

Preprint

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Periprosthetic joint infection (PJI) is a severe complication following joint replacement surgery, often requiring complex multi-stage joint revisions or fusion, and imposing significant burdens on individuals and society as a whole. Accurate diagnosis is crucial for effective treatment. This study aimed to enhance the pathological diagnostic accuracy of PJI by standardizing an intelligent pathological diagnostic process, improving its applicability and practicality in clinical settings, and providing guidance for intelligent diagnosis of infectious diseases. We developed supervised learning models, weakly supervised learning models, and unsupervised learning PJI intelligent classification models and conducted image-level, patient-level testing, and visual verification for the first two models. The weakly supervised learning model performed nearly as well as the supervised learning model in image-level tests, achieving comparable levels of accuracy, recall rates, and ROC curves. However, in patient-level testing, the weakly supervised learning model outperformed its supervised learning counterpart. By adjusting the area threshold for the recognition regions, we significantly improved the sensitivity of PJI pathological diagnosis without compromising specificity (AUC curve area of 0.9460 for the supervised model and 0.9078 for the weakly supervised model). Based on our criteria, the existing diagnostic standard for five high-power fields in a single slide was reduced to only 3. The visualization results also revealed structural changes and loosening in the surrounding tissues, apart from localized neutrophil aggregation. Moreover, the distribution patterns of neutrophil morphology might provide clues for the diagnosis and treatment of PJI. Using an intelligent quantification and statistical approach, we successfully elevated the sensitivity of PJI pathological diagnosis to 88.42% and specificity to 92.31%. Moreover, we established unsupervised rapid auxiliary annotation models, supervised classification models, and unsupervised approximate segmentation models, thereby achieving an intelligent PJI diagnosis. Our study lays the foundation for further intelligent optimization of pathological diagnosis of other infectious diseases.

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Section: The Application Of Arti Cial Intelligence In Image Recogniti...mentioning

confidence: 99%

Clinically applicable optimized periprosthetic joint infection diagnosis via AI-based pathology

Ni,

Tao,

et al. 2024

Preprint

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Deep learning in pulmonary nodule detection and segmentation: a systematic review

Gao,

Wu,

et al. 2024

Eur Radiol

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Objectives The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. Methods This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. Results After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. Conclusions This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. Clinical relevance statement Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. Key Points Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

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Radiomic features add incremental benefit to conventional radiological feature-based differential diagnosis of lung nodules

Liu,

Yang,

Liang

et al. 2024

Eur Radiol

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Efficient pulmonary nodules classification using radiomics and different artificial intelligence strategies

Cited by 10 publications

References 16 publications

Clinically applicable optimized periprosthetic joint infection diagnosis via AI-based pathology

Clinically applicable optimized periprosthetic joint infection diagnosis via AI-based pathology

Deep learning in pulmonary nodule detection and segmentation: a systematic review

Radiomic features add incremental benefit to conventional radiological feature-based differential diagnosis of lung nodules

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