[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4429":3,"related-4429":63},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":37,"slug":38,"view_count":15,"doi":39,"paper":40,"created_at":62},4429,"A modular pancreas segmentation and multimodal fusion framework for clinical and CT decision support in acute pancreatitis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10791-026-10258-y","Timely decision support in emergency imaging requires robust integration of structured clinical variables and contrast-enhanced CT while minimizing reader-dependent processing and improving reproducibility. We propose a modular segmentation-to-fusion framework for multimodal clinical–CT prediction using acute pancreatitis as a case study. The pipeline uses automated pancreas ROI generation with post-segmentation quality control, extracts complementary radiomics and frozen deep features, and integrates multimodal evidence through a calibrated stacked ensemble. We evaluated the framework on a retrospective single-center contrast-enhanced CT cohort using internal training and held-out test splits, assessing discrimination, calibration, and decision-curve performance. The proposed multimodal stacked ensemble achieved an AUC of 0.913 (95% CI 0.840–0.972) on the internal held-out test cohort and showed improved calibration and decision-curve performance compared with comparator models. These results suggest that reproducible multimodal clinical-imaging fusion may provide complementary information for internal risk estimation of AP in a single-center retrospective setting.","急诊影像中的及时决策支持需要稳健地整合结构化临床变量与增强CT，同时尽量减少依赖阅片者的处理并提高可重复性。我们以急性胰腺炎为案例，提出了一种用于多模态临床–CT预测的模块化分割到融合框架。该流程采用自动胰腺感兴趣区（ROI）生成并结合分割后质量控制，提取互补的影像组学特征与冻结深度特征，并通过校准的堆叠集成整合多模态证据。我们在一个回顾性单中心增强CT队列上评估了该框架，采用内部训练与留出测试划分，评估了区分度、校准度和决策曲线性能。所提出的多模态堆叠集成在内部留出测试队列上达到AUC为0.913（95% CI 0.840–0.972），与对照模型相比，校准度和决策曲线性能均有所改善。这些结果表明，在单中心回顾性设置下，可重复的多模态临床–影像融合可为急性胰腺炎（AP）的内部风险估计提供补充信息。",null,"Discover Computing","2026-10-06T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文研究急性胰腺炎的胰腺分割与多模态融合临床决策支持，属于医学影像与临床AI领域，与三农、农业信息化、智慧农业、数字乡村等主题无关，不建议进入每日精选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":36},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":35},"I2",11,true,[33],"decision support",[33],[],"农业相关性需要复核",[],"Amodularpancreassegmentationandmultimoda-4429","10.1007\u002Fs10791-026-10258-y",{"doi":39,"openalex_id":41,"authors":42,"venue":10,"cited_by_count":15,"oa_url":53,"card":54,"direction":60,"ingested_from":61},"W7220712460",[43,45,47,49,51],{"name":44,"orcid":9},"Zhang Gengning",{"name":46,"orcid":9},"Jianqi Li",{"name":48,"orcid":9},"Jie Jiang",{"name":50,"orcid":9},"Yue Yu",{"name":52,"orcid":9},"Jinguang Gu","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10791-026-10258-y.pdf",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"提出模块化胰腺分割与多模态融合框架，结合临床与CT数据预测急性胰腺炎风险。","自动胰腺ROI分割与质控，提取影像组学和冻结深度特征，用校准堆叠集成融合。","多模态堆叠集成AUC达0.913，校准和决策曲线优于对比模型。","农业人工智能与决策模型","可迁移至农业领域，探索作物影像与农艺数据融合的模块化决策支持框架。","农业遥感与作物表型","openalex","2026-10-08T23:30:21.373431Z",{"total":15,"page":64,"page_size":64,"items":65},1,[]]