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Overlooked and under-reported: the impact of cyberattacks on primary care in the UK National Health Service 被忽视和报道不足：网络攻击对英国国家卫生服务初级保健的影响。
IF 24.1 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-07-01 DOI: 10.1016/j.landig.2025.100879
Kunal Rajput , Ara Darzi , Saira Ghafur
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引用次数: 0
External validation of a digital pathology-based multimodal artificial intelligence-derived prognostic model in patients with advanced prostate cancer starting long-term androgen deprivation therapy: a post-hoc ancillary biomarker study of four phase 3 randomised controlled trials of the STAMPEDE platform protocol 在开始长期雄激素剥夺治疗的晚期前列腺癌患者中，基于数字病理的多模式人工智能衍生预后模型的外部验证：STAMPEDE平台方案的四项3期随机对照试验的事后辅助生物标志物研究。
IF 24.1 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-07-01 DOI: 10.1016/j.landig.2025.100885
Charles T A Parker MB BChir , Larissa Mendes PhD , Vinnie Y T Liu MSc , Emily Grist PhD , Songwan Joun MSc , Rikiya Yamashita MD PhD , Akinori Mitani MD PhD , Emmalyn Chen PhD , Marina A Parry PhD , Ashwin Sachdeva PhD , Laura Murphy PhD , Huei-Chung Huang MA , Jacqueline Griffin PhD , Douwe van der Wal MSc , Tamara Todorovic MPH , Sharanpreet Lall BSc , Sara Santos Vidal MSc , Miriam Goncalves BSc , Suparna Thakali BSc , Anna Wingate MSc , Prof Gerhardt Attard PhD

Background

Effective prognostication improves selection of patients with prostate cancer for treatment combinations. We aimed to evaluate whether a previously developed multimodal artificial intelligence (MMAI) algorithm was prognostic in very advanced prostate cancer using data from four phase 3 trials of the STAMPEDE platform protocol.

Methods

We included patients starting androgen-deprivation therapy in the docetaxel, docetaxel plus zoledronic acid, abiraterone, or abiraterone plus enzalutamide trials. Patients were recruited at 112 sites. We combined all standard-of-care control patients (including those allocated to standard of care [SOC-ADT] consisting of testosterone suppression with luteinising hormone-releasing hormone agonists or antagonists, and radiotherapy when indicated), and we combined the rest of the patients into docetaxel-treated or abiraterone-treated groups. Patients had either metastatic disease or were at very high-risk of metastatic disease, determined by node-positivity or, if node-negative, by T stage, serum prostate-specific antigen (PSA) level, and Gleason score. We used the locked ArteraAI Prostate MMAI algorithm that combined these clinical variables, age, and digitised prostate biopsy pathology images. We performed Fine–Gray and Cox regression adjusted for treatment allocation and cumulative incidence analyses at 5 years to evaluate associations with prostate cancer-specific mortality (PCSM) for continuous (per SD increase) and categorical (quartile-Q) scores. The STAMPEDE platform protocol is registered with ClinicalTrials.gov, NCT00268476.

Findings

Of 5213 eligible patients recruited from Oct 5, 2005, to March 31, 2016, 3167 were included in this analysis (1575 [49·7%] with non-metastatic disease, 1592 [50·3%] with metastatic disease; median follow-up 6·9 years [IQR 5·9–8·0]) with all datapoints available for score generation. The MMAI algorithm (per SD increase) was strongly associated with PCSM (hazard ratio [HR] 1·40, 95% CI 1·30–1·51, p<0·0001). On ad-hoc inspection, the highest scoring quartile of patients in each disease and treatment allocation group (MMAI Q4; vs the bottom three quartiles, Q1–3) had the highest PCSM risk in both patients with non-metastatic disease (HR 2·12, 1·61–2·81, p<0·0001) and those with metastatic disease (HR 1·62, 1·39–1·88, p<0·0001). MMAI quartile stratification split patients categorised by disease burden into groups with notably different risks of 5-year PCSM: patients with non-metastatic disease that were node-negative could be further stratified by MMAI score quartile Q1–3 (3%, 2–4) versus Q4 (11%, 7–15), those with non-metastatic disease that were node-positive could be stratified by Q1–3 (11%, 8–14) versus Q4 (20%, 13–26), those with metastatic disease with low-volume could be strati
背景：有效的预后改善了前列腺癌患者联合治疗的选择。我们的目的是评估先前开发的多模态人工智能（MMAI）算法是否可以使用STAMPEDE平台协议的四个3期试验数据来预测晚期前列腺癌的预后。方法：我们纳入了多西他赛、多西他赛加唑来膦酸、阿比特龙或阿比特龙加恩杂鲁胺试验中开始雄激素剥夺治疗的患者。在112个地点招募患者。我们将所有标准护理对照患者（包括那些分配到标准护理[SOC-ADT]的患者，包括睾酮抑制与黄体生成素释放激素激动剂或拮抗剂，并在有指示时进行放疗），并将其余患者合并为多西他赛治疗组或阿比特龙治疗组。患者要么患有转移性疾病，要么处于转移性疾病的高危状态，通过淋巴结阳性或淋巴结阴性，通过T分期、血清前列腺特异性抗原（PSA）水平和Gleason评分来确定。我们使用了锁定的ArteraAI前列腺MMAI算法，该算法结合了这些临床变量、年龄和数字化的前列腺活检病理图像。我们对5年的治疗分配和累积发病率分析进行了微调的Fine-Gray和Cox回归，以评估前列腺癌特异性死亡率（PCSM）与连续（每SD增加）和分类（四分位q）评分的关系。STAMPEDE平台方案已在ClinicalTrials.gov注册，编号NCT00268476。结果：在2005年10月5日至2016年3月31日招募的5213名符合条件的患者中，有3167名患者被纳入该分析，其中1575名（49.7%）为非转移性疾病，1592名（50.3%）为转移性疾病；中位随访时间为6.9年（IQR为5.9 - 8.0），所有数据点均可用于评分生成。MMAI算法（每SD增加）与PCSM密切相关（风险比[HR] 1.40, 95% CI 1.30 - 1.51）。解释：诊断性前列腺活检样本包含放射学上明显转移性前列腺癌患者或高危患者的预后信息。MMAI算法结合疾病负担提高晚期前列腺癌的预后。资助：英国前列腺癌协会、英国医学研究委员会、英国癌症研究中心、约翰·布莱克慈善基金会、前列腺癌基金会、赛诺菲·安万特、杨森、安斯泰来、诺华、Artera。
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引用次数: 0
Development of machine learning prediction models for systemic inflammatory response following controlled exposure to a live attenuated influenza vaccine in healthy adults using multimodal wearable biosensors in Canada: a single-centre, prospective controlled trial 在加拿大使用多模态可穿戴生物传感器的健康成人受控暴露于减弱流感活疫苗后的全身炎症反应的机器学习预测模型的开发：一项单中心、前瞻性对照试验。
IF 24.1 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-07-01 DOI: 10.1016/j.landig.2025.100886
Amir Hadid PhD , Emily G McDonald MD , Qianggang Ding MEng , Christopher Phillipp BSc , Audrey Trottier BSc , Philippe C Dixon PhD , Oussama Jlassi MSc , Matthew P Cheng MD , Jesse Papenburg MD , Prof Michael Libman MD , Dennis Jensen PhD
<div><h3>Background</h3><div>Presymptomatic or asymptomatic immune system signals and subclinical physiological changes might provide a more objective measure of early viral upper respiratory tract infections (VRTIs) compared with symptom-based detection. We aimed to use multimodal wearable sensors, host-response biomarkers, and machine learning to predict systemic inflammation following controlled exposure to a live attenuated influenza vaccine, without relying on symptoms.</div></div><div><h3>Methods</h3><div>WE SENSE study is a single-centre (McGill University Health Center, Montreal, QC, Canada), prospective controlled trial that recruited healthy adults aged 18–59 years who had not received or were not planning to receive the seasonal influenza vaccine or any other vaccine during the study period. We excluded participants with any infectious symptoms within 7 days before screening. We collected physiological and activity data (eg, heart rate, breathing rate, and acceleration) through continuous monitoring with a smart ring (Oura ring Gen 2, Oura Oy, Finland), smart watch (Biobeat watch, Biobeat Technologies, Israel), and smart shirt (Astroskin–Hexoskin shirt, Hexoskin, Canada) along with high temporal resolution systemic inflammatory biomarker mapping over 12 days (7 days before inoculation and 5 days after). We frequently tested participants both before and after inoculation via PCR for respiratory pathogens, and monitored them via apps for symptoms and free-text annotations. Machine learning algorithms predicting systemic inflammatory surges were trained (35 participants), validated (ten participants), and tested (ten participants) using gradient-boosting techniques.</div></div><div><h3>Findings</h3><div>Between Dec 10, 2021, and Feb, 28, 2022, we enrolled 56 participants, of whom 55 had available data; all 55 participants continuously wore the Oura ring, 54 participants wore the Astroskin–Hexoskin shirt, and 50 wore the Biobeat watch. 27 (49%) participants were female and 28 (51%) were male; 31 (56%) participants were White, eight (15%) were Asian, four (7%) were Black, two (4%) were Latino or Hispanic, and ten (18%) did not disclose. We used model 2, which included handpicked features from the Oura ring night-time data, as the candidate model because it was built on the lowest number of features (more practical). This model predicted inflammatory surges with receiver operating characteristic area under the curve (ROC-AUC) of 0·73 (95% CI 0·71–0·74) for real-time prediction and 0·89 (0·87–0·90) for a 24-h tolerance prediction window (24h-tol) using night-time data from the Oura ring. Incorporating both night-time and daytime data from the Astroskin–Hexoskin shirt yielded ROC-AUC values of 0·73 (0·71–0·75) for real-time and 0·91 (0·90–0·92) for 24h-tol along with improved precision (ie, specificity [0·83, 0·79–0·87] and F1 score [0·65, 0·58–0·71]). The model based on symptoms alone had lower performance, with ROC-AUC values of 0·66 (0·63–0
背景：与基于症状的检测相比，症状前或无症状的免疫系统信号和亚临床生理变化可能为早期病毒性上呼吸道感染（VRTIs）提供更客观的衡量标准。我们的目标是使用多模态可穿戴传感器、宿主反应生物标志物和机器学习来预测控制暴露于减毒流感活疫苗后的全身炎症，而不依赖于症状。方法：WE SENSE研究是一项单中心前瞻性对照试验（McGill University Health Center, Montreal， QC, Canada），招募了18-59岁的健康成年人，他们在研究期间没有接种或不打算接种季节性流感疫苗或任何其他疫苗。我们在筛查前7天内排除了有任何感染症状的参与者。我们通过连续监测收集生理和活动数据（例如，心率，呼吸频率和加速度），使用智能环（Oura环Gen 2， Oura Oy，芬兰），智能手表（Biobeat手表，Biobeat Technologies，以色列）和智能衬衫（Astroskin-Hexoskin衬衫，Hexoskin，加拿大），以及高时间分辨率的全身炎症生物标志物制图超过12天（接种前7天和接种后5天）。我们经常通过PCR对接种前后的参与者进行呼吸道病原体检测，并通过应用程序监测他们的症状和免费文本注释。使用梯度增强技术对预测全身性炎症激增的机器学习算法进行了训练（35名参与者）、验证（10名参与者）和测试（10名参与者）。研究结果：在2021年12月10日至2022年2月28日期间，我们招募了56名参与者，其中55名有可用数据；所有55名参与者都一直戴着Oura戒指，54名参与者穿着Astroskin-Hexoskin衬衫，50名参与者戴着Biobeat手表。女性27人（49%），男性28人（51%）；31名（56%）参与者是白人，8名（15%）是亚洲人，4名（7%）是黑人，2名（4%）是拉丁裔或西班牙裔，10名（18%）没有透露。我们使用模型2，其中包括从Oura环夜间数据中精心挑选的特征，作为候选模型，因为它建立在最少数量的特征上（更实用）。该模型使用来自Oura环的夜间数据预测炎症激增，实时预测受试者工作特征曲线下面积（ROC-AUC）为0.73 (95% CI 0.71 - 0.74)， 24小时耐受性预测窗口（24h-tol）为0.89（0.87 - 0.90）。结合astrosskin - hexoskin衬衫的夜间和日间数据，实时的ROC-AUC值为0.73(0.71 - 0.75)，24小时的ROC-AUC值为0.91(0.90 - 0.92)，并提高了精度（即特异性[0.83,0.79 - 0.87]和F1评分[0.65,0.58 - 0.71]）。仅基于症状的模型性能较低，实时ROC-AUC值为0.66 (0.63 - 0.68)，24h-tol的ROC-AUC值为0.79（0.77 - 0.82）。解释：全身炎症生物标志物与可穿戴生物传感器的生理数据相结合，为训练机器学习算法提供了丰富而客观的数据，以预测低级别流感挑战的全身炎症。这种方法优于基于症状的检测，并有可能改善流感等虚拟呼吸道感染的检测，并缩短检测时间，即使在无症状人群中也是如此。资助：加拿大卫生研究所。
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引用次数: 0
Fixing cracks in the artificial intelligence drug development pipeline 修复人工智能药物开发管道中的漏洞。
IF 24.1 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-07-01 DOI: 10.1016/j.landig.2025.100897
The Lancet Digital Health
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引用次数: 0
Medical digital twins: enabling precision medicine and medical artificial intelligence 医疗数字双胞胎：实现精准医疗和医疗人工智能。
IF 24.1 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-07-01 DOI: 10.1016/j.landig.2025.02.004
Christoph Sadée MRes , Stefano Testa MD , Thomas Barba MD PhD , Katherine Hartmann MD PhD , Maximilian Schuessler MS MPP , Alexander Thieme Dr med , Prof George M Church PhD , Prof Ifeoma Okoye MBBS FWACS , Prof Tina Hernandez-Boussard PhD , Prof Leroy Hood MD PhD , Prof Ilya Shmulevich PhD , Prof Ellen Kuhl PhD , Prof Olivier Gevaert PhD
The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.
医学数字双胞胎的概念在科学界和公众中越来越受欢迎；然而，最近的热情在很大程度上是在对它们的基本构成缺乏共识的情况下出现的。数字双胞胎起源于工程领域，其中不断更新的虚拟副本可以对现实世界的对象或过程进行分析、模拟和预测。在这篇健康政策论文中，我们在医学背景下评估了这一概念，并概述了医疗数字孪生的五个关键组成部分：患者、数据连接、硅片患者、接口和孪生同步。我们考虑了多模态数据、人工智能和机械建模中的各种使能技术将如何为临床应用铺平道路，并提供与肿瘤学和糖尿病相关的示例。我们强调了数据融合的作用以及将人工智能和机械建模相结合的潜力，以解决独立使用人工智能或机械建模方法的局限性。我们特别强调了数字孪生概念如何支持医学中应用的大型语言模型的性能及其解决医疗保健挑战的潜力。我们相信，这份卫生政策文件将有助于指导科学家、临床医生和政策制定者在未来创造医疗数字双胞胎，并将这一有前途的新范式从理论转化为临床实践。
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引用次数: 0
When data disappear: public health pays as US policy strays 当数据消失时：公共卫生因美国政策偏离而付出代价。
IF 24.1 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-07-01 DOI: 10.1016/j.landig.2025.100874
Thomas McAndrew PhD , Andrew A Lover PhD , Garrik Hoyt , Maimuna S Majumder PhD
Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources; and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good.
唐纳德·特朗普总统于2025年1月20日采取的总统行动，包括行政命令，推迟了对美国关键公共卫生数据源的访问或导致其被删除。持续收集和维护卫生数据有助于与季节性流感等疾病相关的公共卫生、安全和保障。为了显示公共卫生数据监测如何加强公共卫生实践，我们分析了来自美国政府维护的与季节性流感相关的七个来源的数据。我们拟合了两个模型来预测美国全国突发流感住院人数：(1)一个数据丰富的模型，包含来自所有七个政府数据源的数据；(2)使用单一政府住院数据来源建立的缺乏数据的模型，仅代表产生流感住院预测所需的最低限度信息。数据丰富的模型产生了对公共卫生决策有用的可靠预测，而使用数据贫乏模型的预测高度不确定，使其不切实际。因此，卫生数据可以作为改善国内和全球卫生的透明和标准化基础。因此，应制定一项计划，将公共卫生数据作为一项公益事业加以保护。
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引用次数: 0
One shot at trust: building credible evidence for medical artificial intelligence 获得信任的一个机会是：为医疗人工智能建立可信的证据。
IF 24.1 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-07-01 DOI: 10.1016/j.landig.2025.100883
Ramez Kouzy , Julian C Hong , Danielle S Bitterman
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引用次数: 0
Early detection of sexually transmitted infections from skin lesions with deep learning: a systematic review and meta-analysis 深度学习在皮肤损伤性传播感染早期检测中的应用：一项系统综述和荟萃分析。
IF 24.1 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-07-01 DOI: 10.1016/j.landig.2025.100894
Ming Liu MSc , Xin-Yao Yi MSc , Yun-Zhe Chen MPH , Mei-Nuo Li MPH , Yuan-Yuan Zhang MPH , Casper J P Zhang PhD , Jian Huang PhD , Prof Wai-Kit Ming MD

Background

Sexually transmitted infections (STIs) are a substantial public health concern. We aimed to evaluate the accuracy and applicability of deep learning algorithms in the early detection of STIs from skin lesions.

Methods

In this systematic review and meta-analysis, we searched PubMed, Institute of Electrical and Electronics Engineers Xplore, Web of Science, Scopus for studies employing deep learning for classifying clinical skin lesion images of STIs published between Jan 1, 2010, and Dec 31, 2023. Studies that did not include clinical images were excluded. The primary outcome was diagnostic performance, assessed by pooled sensitivity and specificity. We conducted a meta-analysis of the studies providing contingency tables using a unified hierarchical model. We additionally assessed the quality of the studies using modified QUADAS-2 and CheckList for Evaluation of image-based AI Reports in Dermatology (CLEAR Derm) criteria. This study was registered with PROSPERO, CRD42024496966.

Findings

Among the 1946 studies identified, we included 101 in our review. The majority of the included studies focused on mpox (91 [88%] of 101 studies), followed by scabies (eight [8%] studies), herpes (four [4%] studies), syphilis (one [1%] study), and molluscum (one [1%] study). A meta-analysis of 55 studies showed that deep learning algorithms had a pooled sensitivity of 0·97 (95% CI 0·95–0·98) and a specificity of 0·99 (0·98–0·99) for mpox, and a sensitivity of 0·95 (0·90–0·98) and specificity of 0·97 (0·86–0·99) for scabies. The majority of studies (86 [85%] of 101 studies) utilised public datasets; traditional convolutional neural networks with backbone architectures such as ResNet and VGGNet were used in all studies. However, notable quality issues related to the data, technical descriptions of labelling methods and diagnostic label references, technical assessment for public evaluation of algorithms, benchmarking and bias assessments, application descriptions of use cases, and target conditions and potential impacts were identified in CLEAR Derm. Potential biases in performance evaluation metrics and applicability concerns in the data, deep learning algorithms, and performance evaluation metrics might impede the generalisability of these models to real-world clinical practice and STI screening across diverse populations.

Interpretation

Although deep learning shows potential for early detection of STIs, there are challenges to ensuring the generalisability of such algorithms due to limited heterogeneous data. Standardised, diverse skin lesion image datasets are crucial to ensure fair comparisons and reliable performance.

Funding

City University of Hong Kong.
背景：性传播感染（STIs）是一个重大的公共卫生问题。我们的目的是评估深度学习算法在皮肤病变性传播感染早期检测中的准确性和适用性。方法：在本系统综述和荟萃分析中，我们检索了PubMed， Institute of Electrical and Electronics Engineers Xplore, Web of Science, Scopus，检索了2010年1月1日至2023年12月31日期间发表的使用深度学习对性传播感染临床皮肤病变图像进行分类的研究。不包括临床影像的研究被排除在外。主要结局是诊断表现，通过综合敏感性和特异性进行评估。我们对这些研究进行了荟萃分析，使用统一的层次模型提供列联表。我们还使用改进的QUADAS-2和基于图像的皮肤病学人工智能报告评估清单（CLEAR Derm）标准评估了研究的质量。本研究注册号为PROSPERO， CRD42024496966。研究结果：在已确定的1946项研究中，我们纳入了101项。纳入的大多数研究集中于mpox(101项研究中的91项[88%])，其次是疥疮（8项[8%]研究）、疱疹（4项[4%]研究）、梅毒（1项[1%]研究）和软疣（1项[1%]研究）。55项研究的荟萃分析显示，深度学习算法对m痘的总灵敏度为0.97 (95% CI为0.95 ~ 0.98)，特异性为0.99(0.98 ~ 0.99)；对疥疮的总灵敏度为0.95(0.90 ~ 0.98)，特异性为0.97（0.86 ~ 0.99）。大多数研究（101项研究中的86项[85%]）使用了公共数据集；所有研究均使用具有骨干结构的传统卷积神经网络，如ResNet和VGGNet。然而，在CLEAR Derm中发现了与数据、标签方法和诊断标签参考的技术描述、算法公共评估的技术评估、基准和偏差评估、用例的应用描述以及目标条件和潜在影响相关的显著质量问题。性能评估指标的潜在偏差以及数据、深度学习算法和性能评估指标的适用性问题可能会阻碍这些模型在现实世界的临床实践和不同人群的STI筛查中的推广。解释：尽管深度学习显示出早期发现性传播感染的潜力，但由于异构数据有限，在确保此类算法的通用性方面存在挑战。标准化、多样化的皮肤病变图像数据集对于确保公平比较和可靠的性能至关重要。资助：香港城市大学。
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引用次数: 0
FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication: a model development and validation study FaceAge，一个深度学习系统，从面部照片估计生物年龄，以提高预测：一项模型开发和验证研究。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-06-01 DOI: 10.1016/j.landig.2025.03.002
Dennis Bontempi PhD , Osbert Zalay PhD , Danielle S Bitterman MD , Nicolai Birkbak PhD , Derek Shyr PhD , Fridolin Haugg MSc , Jack M Qian MD , Hannah Roberts MD , Subha Perni MD , Vasco Prudente MSc , Suraj Pai MSc , Andre Dekker PhD , Benjamin Haibe-Kains PhD , Christian Guthier PhD , Tracy Balboni MD , Laura Warren MD , Monica Krishan MD , Benjamin H Kann MD , Prof Charles Swanton MD , Prof Dirk De Ruysscher MD , Prof Hugo J W L Aerts PhD
<div><h3>Background</h3><div>As humans age at different rates, physical appearance can yield insights into biological age and physiological health more reliably than chronological age. In medicine, however, appearance is incorporated into medical judgements in a subjective and non-standardised way. In this study, we aimed to develop and validate FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs.</div></div><div><h3>Methods</h3><div>FaceAge was trained on data from 58 851 presumed healthy individuals aged 60 years or older: 56 304 individuals from the IMDb–Wiki dataset (training) and 2547 from the UTKFace dataset (initial validation). Clinical utility was evaluated on data from 6196 patients with cancer diagnoses from two institutions in the Netherlands and the USA: the MAASTRO, Harvard Thoracic, and Harvard Palliative cohorts FaceAge estimates in these cancer cohorts were compared with a non-cancerous reference cohort of 535 individuals. To assess the prognostic relevance of FaceAge, we performed Kaplan–Meier survival analysis and Cox modelling, adjusting for several clinical covariates. We also assessed the performance of FaceAge in patients with metastatic cancer receiving palliative treatment at the end of life by incorporating FaceAge into clinical prediction models. To evaluate whether FaceAge has the potential to be a biomarker for molecular ageing, we performed a gene-based analysis to assess its association with senescence genes.</div></div><div><h3>Findings</h3><div>FaceAge showed significant independent prognostic performance in various cancer types and stages. Looking older was correlated with worse overall survival (after adjusting for covariates per-decade hazard ratio [HR] 1·151, p=0·013 in a pan-cancer cohort of n=4906; 1·148, p=0·011 in a thoracic cohort of n=573; and 1·117, p=0·021 in a palliative cohort of n=717). We found that, on average, patients with cancer looked older than their chronological age (mean increase of 4·79 years with respect to non-cancerous reference cohort, p<0·0001). We found that FaceAge can improve physicians’ survival predictions in patients with incurable cancer receiving palliative treatments (from area under the curve 0·74 [95% CI 0·70–0·78] to 0·8 [0·76–0·83]; p<0·0001), highlighting the clinical use of the algorithm to support end-of-life decision making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, whereas age was not.</div></div><div><h3>Interpretation</h3><div>Our results suggest that a deep learning model can estimate biological age from face photographs and thereby enhance survival prediction in patients with cancer. Further research, including validation in larger cohorts, is needed to verify these findings in patients with cancer and to establish whether the findings extend to patients with other diseases. Subject to further testing and validation, approaches such as
背景：随着人类以不同的速度衰老，外表可以比实足年龄更可靠地反映生物年龄和生理健康状况。然而，在医学上，外表以一种主观和非标准化的方式被纳入医学判断。在这项研究中，我们旨在开发和验证FaceAge，这是一个深度学习系统，可以从容易获得的低成本面部照片中估计生物年龄。方法：FaceAge使用来自58 851名60岁或以上的假定健康个体的数据进行训练，其中56 304人来自IMDb-Wiki数据集（训练），2547人来自UTKFace数据集（初始验证）。临床应用评估了来自荷兰和美国两个机构的6196名癌症诊断患者的数据：MAASTRO、哈佛胸廓和哈佛姑息治疗队列。将这些癌症队列的FaceAge估计与535名非癌症参考队列进行比较。为了评估FaceAge的预后相关性，我们进行了Kaplan-Meier生存分析和Cox建模，调整了几个临床协变量。我们还通过将FaceAge纳入临床预测模型，评估了FaceAge在晚期接受姑息治疗的转移性癌症患者中的表现。为了评估FaceAge是否有潜力成为分子衰老的生物标志物，我们进行了一项基于基因的分析，以评估其与衰老基因的关联。结果：FaceAge在不同癌症类型和分期中显示出显著的独立预后表现。年龄越大与总生存率越差相关(在n=4906的泛癌症队列中，校正协变量后，每十年的风险比[HR]为1.151,p= 0.013；1·148,p=0·011在胸部队列n=573；在姑息治疗队列（n=717）中为1·117，p=0·021。我们发现，平均而言，癌症患者看起来比他们的实际年龄要老（与非癌症参考队列相比，平均增加4.79岁）。解释：我们的研究结果表明，深度学习模型可以从面部照片中估计生物年龄，从而提高癌症患者的生存预测。需要进一步的研究，包括在更大的队列中进行验证，以验证癌症患者的这些发现，并确定这些发现是否适用于其他疾病患者。经过进一步的测试和验证，FaceAge等方法可用于将患者的视觉外观转化为客观、定量和有临床价值的测量。资助：美国国立卫生研究院和欧盟欧洲研究理事会。
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引用次数: 0
Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model 以变压器为基础的风险模型对个体进行预防性心血管疾病治疗的精细选择。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS
Lancet Digital Health
Pub Date : 2025-06-01 DOI: 10.1016/j.landig.2025.03.005
Shishir Rao DPhil , Yikuan Li DPhil , Mohammad Mamouei PhD , Gholamreza Salimi-Khorshidi DPhil , Malgorzata Wamil PhD , Milad Nazarzadeh DPhil , Christopher Yau DPhil , Gary S Collins PhD , Rod Jackson PhD , Andrew Vickers DPhil , Goodarz Danaei MD ScD , Kazem Rahimi DM FESC

Background

Although statistical models have been commonly used to identify patients at risk of cardiovascular disease for preventive therapy, these models tend to over-recommend therapy. Moreover, in populations with pre-existing diseases, the current approach is to indiscriminately treat all, as modelling in this context is currently inadequate. This study aimed to develop and validate the Transformer-based Risk assessment survival (TRisk) model, a novel deep learning model, for predicting 10-year risk of cardiovascular disease in both the primary prevention population and individuals with diabetes.

Methods

An open cohort of 3 million adults aged 25–84 years was identified using linked electronic health records from 291 general practices, for model development, and 98 general practices, for validation, across England from 1998 to 2015. Comparison against the QRISK3 score and a deep learning derivation of it was done. Additional analyses compared discriminatory performance in other age groups, by sex, and across categories of socioeconomic status.

Findings

TRisk showed superior discrimination (C index in the primary prevention population 0·910; 95% CI 0·906–0·913). TRisk’s performance was found to be less sensitive to population age range than the benchmark models and outperformed other models also in analyses stratified by age, sex, or socioeconomic status. All models were overall well calibrated. In decision curve analyses, TRisk showed a greater net benefit than benchmark models across the range of relevant thresholds. At the widely recommended 10% risk threshold and the higher 15% threshold, TRisk reduced both the total number of patients classified at high risk (by 20·6% and 34·6%, respectively) and the number of false negatives as compared with recommended strategies. TRisk similarly outperformed other models in patients with diabetes. Compared with the widely recommended treat-all policy approach for patients with diabetes, TRisk at a 10% risk threshold would lead to deselection of 24·3% of individuals, with a small fraction of false negatives (0·2% of the cohort).

Interpretation

TRisk enabled a more targeted selection of individuals at risk of cardiovascular disease in both the primary prevention population and cohorts with diabetes, compared with benchmark approaches. Incorporation of TRisk into routine care could potentially reduce the number of treatment-eligible patients by approximately one-third while preventing at least as many events as with currently adopted approaches.

Funding

None.
背景：虽然统计模型通常用于识别心血管疾病风险患者进行预防治疗，但这些模型倾向于过度推荐治疗。此外，在已有疾病的人群中，目前的做法是不分青红皂白地治疗所有人，因为在这方面的建模目前是不充分的。本研究旨在开发和验证基于transformer的风险评估生存（TRisk）模型，这是一种新的深度学习模型，用于预测初级预防人群和糖尿病患者10年心血管疾病风险。方法：从1998年至2015年，使用英格兰291个全科诊所的相关电子健康记录确定了300万名25-84岁成年人的开放队列，用于模型开发，98个全科诊所进行验证。与QRISK3分数进行了比较，并对其进行了深度学习推导。其他分析比较了其他年龄组、性别和不同社会经济地位类别的歧视性表现。结果：一级预防人群的风险指数（C指数）为0·910；95% ci 0.906 - 0.913)。研究发现，与基准模型相比，风险模型对人口年龄范围的敏感性较低，在按年龄、性别或社会经济地位分层的分析中，风险模型的表现也优于其他模型。所有模型总体上都得到了很好的校准。在决策曲线分析中，在相关阈值范围内，TRisk显示出比基准模型更大的净收益。在广泛推荐的10%风险阈值和更高的15%阈值下，与推荐的策略相比，TRisk降低了高风险患者的总数（分别减少20.6%和34.6%）和假阴性的数量。在糖尿病患者中，TRisk同样优于其他模型。与广泛推荐的针对糖尿病患者的全面治疗政策方法相比，风险阈值为10%的风险将导致24.3%的个体取消选择，并有一小部分假阴性（0.2%的队列）。解释：与基准方法相比，在初级预防人群和糖尿病人群中，风险使得更有针对性地选择有心血管疾病风险的个体。将风险纳入常规护理可能会使符合治疗条件的患者数量减少约三分之一，同时预防的事件至少与目前采用的方法一样多。资金:没有。
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