[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4746":3,"related-4746":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":38,"slug":39,"view_count":15,"doi":40,"paper":41,"created_at":62},4746,"Artificial Intelligence and Imaging Technologies in Orthodontic Assessment, Referral and Care Pathways: A Scoping Review","https:\u002F\u002Fdoi.org\u002F10.12688\u002Ff1000research.190748.1","Abstract* Background Artificial intelligence (AI), integrated with imaging technologies, is increasingly applied across orthodontic assessment and care pathways. However, the evidence remains scattered across imaging modalities, clinical applications, AI methodologies, and stages of clinical translation. Methods A scoping review was conducted in accordance with the Joanna Briggs Institute methodology and reported in line with the PRISMA-ScR guidelines. PubMed, Scopus, and IEEE Xplore were searched for studies published between January 2016 and June 2026, with searches supplemented by Google Scholar, OpenAlex, grey literature sources, and citation searching. Eligible studies were mapped descriptively and synthesised thematically using Braun and Clarke’s framework. Results Fifty-four studies were included. AI applications used intraoral and facial photographs, cephalometric radiographs, CBCT, intraoral scans, three-dimensional dental models, and multimodal imaging. Applications ranged from landmark localisation, segmentation, and malocclusion classification to treatment-need assessment, screening, referral support, treatment planning, and remote monitoring. Evidence was strongest for bounded, anatomically verifiable image-analysis tasks, whereas applications involving referral, triage, treatment-need decisions, longitudinal prediction, and workflow integration were less mature. Common limitations included retrospective, single-centre designs; heterogeneous reference standards; limited external validation; inconsistent reporting of calibration and uncertainty; and insufficient evaluation of patient-centred, equity, and service-level outcomes. Conclusions Imaging-based AI in orthodontics has evolved from automated image interpretation to clinician-assisted decision support across the care pathway. Current evidence most strongly supports AI for structured image-analysis tasks, but does not yet support autonomous referral, triage, or treatment-planning decisions. Future research should prioritise prospective multicentre validation, transparent reporting, explainability, fairness, uncertainty handling, and workflow-integrated evaluation to enable safe and clinically useful implementation.","摘要* 背景 人工智能（AI）与成像技术的融合正日益应用于正畸评估与诊疗路径的各个环节。然而，现有证据分散于不同的成像模态、临床应用、AI方法学及临床转化阶段之中。方法 按照乔安娜布里格斯研究所（Joanna Briggs Institute）方法学开展范围综述，并依据PRISMA-ScR指南进行报告。检索了PubMed、Scopus和IEEE Xplore中2016年1月至2026年6月发表的研究，并通过Google Scholar、OpenAlex、灰色文献来源及引文检索进行补充。对符合条件的研究进行描述性映射，并采用Braun和Clarke框架进行主题综合。结果 共纳入54项研究。AI应用涉及口内及面部照片、头影测量X线片、CBCT、口内扫描、三维牙颌模型和多模态成像。应用范围从标志点定位、分割和错𬌗畸形分类，到治疗需求评估、筛查、转诊支持、治疗计划和远程监测。证据在边界明确、解剖学可验证的图像分析任务中最为充分，而涉及转诊、分诊、治疗需求决策、纵向预测和工作流程整合的应用则较不成熟。常见局限性包括回顾性、单中心设计；参考标准异质性；外部验证有限；校准和不确定性报告不一致；以及对以患者为中心、公平性和服务层面结局的评估不足。结论 正畸领域基于成像的AI已从自动化图像解读发展为贯穿诊疗路径的临床医生辅助决策支持。当前证据最有力地支持AI用于结构化图像分析任务，但尚不支持自主转诊、分诊或治疗计划决策。未来研究应优先开展前瞻性多中心验证、透明报告、可解释性、公平性、不确定性处理及工作流程整合评估，以实现安全且具有临床实用价值的应用。",null,"F1000Research","2026-10-10T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为口腔正畸影像AI综述，属医学领域，与三农、农业信息化、智慧农业无关，不予入选。",[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":37},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":36},"I2",14,true,[33],"artificial intelligence",[33,35],"decision support",[],"农业相关性需要复核",[],"ArtificialIntelligenceandImagingTechnolo-4746","10.12688\u002Ff1000research.190748.1",{"doi":40,"openalex_id":42,"authors":43,"venue":10,"cited_by_count":15,"oa_url":53,"card":54,"direction":60,"ingested_from":61},"W7221361066",[44,47,50],{"name":45,"orcid":46},"Sarah Kayali","https:\u002F\u002Forcid.org\u002F0009-0005-7601-1325",{"name":48,"orcid":49},"Eduardo Bernabé","https:\u002F\u002Forcid.org\u002F0000-0002-1858-3713",{"name":51,"orcid":52},"Ali Golkari","https:\u002F\u002Forcid.org\u002F0000-0002-6779-7902","https:\u002F\u002Ff1000research.com\u002Farticles\u002F15-1697\u002Fpdf",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"综述正畸诊疗路径中AI与影像技术的应用证据，覆盖评估、转诊与治疗决策。","遵循JBI与PRISMA-ScR的范围综述，检索三大数据库并主题综合54项研究。","AI在结构化图像分析任务上证据最强，尚不支持自主转诊、分诊或治疗规划决策。","其他","可延伸至前瞻性多中心验证、可解释性与公平性评估，以及工作流整合的临床落地研究。","农业遥感与作物表型","openalex","2026-10-12T23:30:18.557276Z",{"total":15,"page":64,"page_size":64,"items":65},1,[]]