BMC medical imaging · 2026
Selection of proper artificial intelligence techniques developed for CT scan image analysis of liver cancer using fuzzy AHP-TOPSIS.
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Lu Geng, Wang Yuheng, Li Chunying, Wu Cong
Аннотация
OBJECTIVE: Comprehensive identification and prioritization of developed artificial intelligence methods for applications of radiology can help to select proper techniques. This study aimed to introduce the best artificial intelligence techniques developed for CT scan image analysis of liver cancer using fuzzy AHP-TOPSIS.
MATERIALS AND METHODS: To identify the artificial intelligence techniques developed, a systematic search was performed in five reliable databases. The developed methods were categorized into four groups based on their application type. After that, the Delphi method was applied in two rounds to determine the proper criteria for selecting the best artificial intelligence techniques. To estimate the relative weights of the criteria also, the fuzzy analytical hierarchy process (FAHP) method was used. In the next step, to prioritize the identified artificial intelligence techniques, the technique for order of preference by similarity to the ideal solution (TOPSIS) method was applied.
RESULTS: 260 artificial intelligence techniques were identified. Seven selection criteria of validity, accuracy, comprehensiveness, processing time, cost, simplicity, and executive capability were introduced. The deep learning-based image reconstruction model (weight = 0.896) in the group of detection and diagnosis, dual-energy CT deep learning radiomics model (weight = 0.862) in the group of prediction, prognosis, and registration, hybrid densely connected UNet (H-DenseUNet) technique (weight = 0.888) in the group of segmentation and classification, and deep learning, radiomics, and clinical (DLRC) model (weight = 0.956) in the group of treatment and therapy were selected as the proper methods.
CONCLUSIONS: These findings represent a total approach to the developed techniques which can be used for designing methods with better performance in the future.
MATERIALS AND METHODS: To identify the artificial intelligence techniques developed, a systematic search was performed in five reliable databases. The developed methods were categorized into four groups based on their application type. After that, the Delphi method was applied in two rounds to determine the proper criteria for selecting the best artificial intelligence techniques. To estimate the relative weights of the criteria also, the fuzzy analytical hierarchy process (FAHP) method was used. In the next step, to prioritize the identified artificial intelligence techniques, the technique for order of preference by similarity to the ideal solution (TOPSIS) method was applied.
RESULTS: 260 artificial intelligence techniques were identified. Seven selection criteria of validity, accuracy, comprehensiveness, processing time, cost, simplicity, and executive capability were introduced. The deep learning-based image reconstruction model (weight = 0.896) in the group of detection and diagnosis, dual-energy CT deep learning radiomics model (weight = 0.862) in the group of prediction, prognosis, and registration, hybrid densely connected UNet (H-DenseUNet) technique (weight = 0.888) in the group of segmentation and classification, and deep learning, radiomics, and clinical (DLRC) model (weight = 0.956) in the group of treatment and therapy were selected as the proper methods.
CONCLUSIONS: These findings represent a total approach to the developed techniques which can be used for designing methods with better performance in the future.