BMC medicine · 2026
Refined AI-ASPECTS with modified atlas and lesion-load thresholds: advancing acute ischemic stroke imaging and prognostic prediction.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Jiang Liang, Deng Xin, Li Changcheng, Zhou Yi, Huang Hongjie, Xu Qiang, Cao Zehong, Nie Ziwei, Cheng Xiaoqing, Shi Yinghua, Peng Mingyang, Deng Qiwen, Fang Xiangming, Pan Changjie, Ye Jing, Jiang Zhen, Guo Caifeng, Mantini Dante, Ding Zhongxiang, Lu Guangming, Shi Feng, Yin Xindao, Wang Shukui, Zhu Wusheng, Zhang Zhiqiang, Multi-Center Cooperative Clinical Study Group for “Lesion-Phenotype Brain Mapping in Acute Stroke”
Аннотация
BACKGROUND: The artificial intelligence-assisted ASPECTS (AI-ASPECTS) system has become an increasingly common tool in clinical practice for assessing acute ischemic stroke (AIS). However, current AI-ASPECTS implementations still rely on the conventional expert-evaluation framework, which uses a simplified two-slice atlas and arbitrarily selected lesion-load thresholds. Our study aimed to develop a refined AI-assisted ASPECTS (Ref-AI-ASPECTS) framework featuring a seamless & whole middle cerebral artery (MCA) territory atlas and region-specific, optimally determined lesion-load thresholds, and comprehensively evaluate the performance of this framework across various clinical scenarios for AIS.
METHODS: We enrolled a cohort of 7,655 AIS patients from eleven centers. Modified atlas was created by expanding conventional atlas based on full MCA territory. Ref-AI-ASPECTS with modified atlas and specific lesion-load thresholds was established using a genetic algorithm. The clinical utility of Ref-AI-ASPECTS was assessed by comparing it to the conventional framework (Con-AI-ASPECTS) in terms of correlation with NIHSS scores on admission, dichotomized prediction of mRS at 3 months, and consistency with expert scoring across the training DWI data, external DWI data, expanded CT data, and real-world prospective DWI data.
RESULTS: The Ref-AI-ASPECTS frameworks with modified atlas and specific lesion-load thresholds (2% to 29%) achieved correlation coefficients (r) of -0.414/-0.438/-0.375 and AUC values of 0.665/0.723/0.707 in the training/internal validation/external validation sets, surpassing both Con-AI- (r: -0.336/-0.402/-0.331; AUC: 0.615/0.654/0.654) and expert-ASPECTS (r: -0.196/-0.206/-0.173; AUC: 0.600/0.641/0.644) (all P < 0.01). The intraclass correlation coefficients for expert- and Ref-AI-ASPECTS were 0.82 and 0.81 in the training and external validation DWI sets, respectively, exceeding those of expert- and Con-AI-ASPECTS (0.69/0.67; both P < 0.01). These improvements were consistently validated across expanded CT datasets (AUC: 0.696 and 0.679) and in a real-world prospective cohort (AUC: 0.710).
CONCLUSIONS: The Ref-AI-ASPECTS framework outperformed conventional approaches in evaluating baseline neurological deficits and predicting functional outcomes in AIS. Our findings support the potential for its wider implementation in AI-ASPECTS systems. Prospective external real‑world validation remains necessary.
TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04775147; chictr.org.cn Identifier: ChiCTR2400092230.
METHODS: We enrolled a cohort of 7,655 AIS patients from eleven centers. Modified atlas was created by expanding conventional atlas based on full MCA territory. Ref-AI-ASPECTS with modified atlas and specific lesion-load thresholds was established using a genetic algorithm. The clinical utility of Ref-AI-ASPECTS was assessed by comparing it to the conventional framework (Con-AI-ASPECTS) in terms of correlation with NIHSS scores on admission, dichotomized prediction of mRS at 3 months, and consistency with expert scoring across the training DWI data, external DWI data, expanded CT data, and real-world prospective DWI data.
RESULTS: The Ref-AI-ASPECTS frameworks with modified atlas and specific lesion-load thresholds (2% to 29%) achieved correlation coefficients (r) of -0.414/-0.438/-0.375 and AUC values of 0.665/0.723/0.707 in the training/internal validation/external validation sets, surpassing both Con-AI- (r: -0.336/-0.402/-0.331; AUC: 0.615/0.654/0.654) and expert-ASPECTS (r: -0.196/-0.206/-0.173; AUC: 0.600/0.641/0.644) (all P < 0.01). The intraclass correlation coefficients for expert- and Ref-AI-ASPECTS were 0.82 and 0.81 in the training and external validation DWI sets, respectively, exceeding those of expert- and Con-AI-ASPECTS (0.69/0.67; both P < 0.01). These improvements were consistently validated across expanded CT datasets (AUC: 0.696 and 0.679) and in a real-world prospective cohort (AUC: 0.710).
CONCLUSIONS: The Ref-AI-ASPECTS framework outperformed conventional approaches in evaluating baseline neurological deficits and predicting functional outcomes in AIS. Our findings support the potential for its wider implementation in AI-ASPECTS systems. Prospective external real‑world validation remains necessary.
TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04775147; chictr.org.cn Identifier: ChiCTR2400092230.