Four internationally validated clinical screening engines work together to provide a comprehensive view of your internal health.
心血管健康測算Cardiovascular Health
MediwhaleMediwhale
冠心病風險分層評估
Coronary Heart Disease Risk Stratification Assessment
透過非侵入性眼底微血管動態追蹤，優於現有的心血管疾病事件風險分層方法，有潛力作為 CACS 的替代測量方法，評估未來心血管疾病及血管硬化風險。
臨床效益：提早防範心肌梗塞。
Utilizing non-invasive tracking of retinal microvascular dynamics, this method outperforms existing cardiovascular event risk stratification approaches. It holds significant potential as an alternative measurement to the Coronary Artery Calcium Score (CACS) for evaluating future cardiovascular disease and arterial stiffness risks.
Clinical Benefit: Early prevention of myocardial infarction.
學術文獻Academic Literature
腦健康Brain Health
iCogiCog
大腦健康與神經生物標誌物篩查
Brain Health & Neuro-Biomarker Screening
評估視網膜神經結構的細微退化現象，這些變化通常與早期阿茲海默症等認知障礙密切相關。
臨床效益：基於視網膜照片的深度學習演算法能以良好準確度偵測阿茲海默症，顯示其在社區環境中篩查阿茲海默症的潛力。
Evaluates subtle degenerative changes in retinal neural structures, which are closely linked to early cognitive impairments such as Alzheimer’s disease.
Clinical Benefit: Deep-learning algorithms based on retinal photographs can detect Alzheimer’s disease with high accuracy, demonstrating strong potential for community-based screening.
學術文獻Academic Literature
眼健康Eye Health
EyRisEyRis
隱形視力殺手精準篩查
Precision Screening for Silent Vision Killers
快速篩查，敏感度方面展現出明顯優於人類表現，糖尿病視網膜病變、青光眼及黃斑部病變，及早辨識可能影響視力的潛在風險。
臨床效益：預防提早篩查糖尿病視網膜病變及其他眼疾。
A rapid screening method with sensitivity significantly outperforming human expert performance. It enables early identification of potential vision-threatening risks, including Diabetic Retinopathy, Glaucoma, and Macular Degeneration.
Clinical Benefit: Early screening and prevention of diabetic retinopathy and other major ocular conditions.
學術文獻Academic Literature
生物年齡Biological Age
RetiPhenoAgeRetiPhenoAge
細胞生物學年齡與衰老速率檢測
Cellular Biological Age & Aging Rate Assessment
深度學習衍生生物老化標記，做為死亡率和發病率結果的穩健預測因子，並可作為測量老化的新型非侵入性方法。
臨床效益：制定個人化的精準抗衰老與長壽管理規劃。
Uses deep learning-derived biological aging biomarkers as robust predictors of morbidity and mortality outcomes, offering a novel, non-invasive method for measuring biological aging trajectories.
Clinical Benefit: Formulating personalized precision anti-aging and longevity management plans.
學術文獻Academic Literature
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Gunasekeran DV, Miller S, Hsu W, et al.
National Use of Artificial Intelligence for Eye Screening in Singapore.
NEJM AI. 2024.
doi:10.1056/AIcs2400404.
此案例研究介紹 SELENA+ 在新加坡全國糖尿病視網膜病變篩查中的部署與真實世界表現。
This case study describes national deployment and real-world performance of SELENA+ for diabetic retinopathy screening in Singapore.
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Cheung CY, Ran AR, Wang S, et al.
A Deep Learning Model for Detection of Alzheimer’s Disease Based on Retinal Photographs: A Retrospective, Multicentre Case-Control Study.
The Lancet Digital Health. 2022;4:e806–e815.
doi:10.1016/S2589-7500(22)00169-8.
研究顯示，深度學習可利用視網膜照片辨識阿茲海默症相關訊號，並於多中心資料中測試。
The study shows that deep learning can identify Alzheimer’s disease-related signals from retinal photographs and was tested across multiple centres.
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Rim TH, Lee CJ, Tham YC, et al.
Deep-Learning-Based Cardiovascular Risk Stratification Using Coronary Artery Calcium Scores Predicted from Retinal Photographs.
The Lancet Digital Health. 2021;3:e306–e316.
doi:10.1016/S2589-7500(21)00043-1.
研究以視網膜照片推算冠狀動脈鈣化風險，並評估其在心血管風險分層中的應用。
The study used retinal photographs to estimate coronary artery calcium-related risk and evaluated its role in cardiovascular risk stratification.
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Nusinovici S, Rim TH, Li H, et al.
Application of a Deep-Learning Marker for Morbidity and Mortality Prediction Derived from Retinal Photographs: A Cohort Development and Validation Study.
The Lancet Healthy Longevity. 2024;5:100593.
doi:10.1016/S2666-7568(24)00089-8.
研究介紹 RetiPhenoAge，一種由視網膜照片推算的深度學習生物年齡標記。模型以 UK Biobank 資料開發，並於新加坡及美國外部驗證，顯示可預測生物年齡、心血管疾病、發病率及死亡率。
Introduces RetiPhenoAge, a deep learning-based biological ageing marker derived from retinal photographs. The model was developed using UK Biobank data and externally validated in Singapore and the USA, demonstrating strong prediction of biological ageing, cardiovascular disease, morbidity, and mortality.
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Cho J, Seo J, Park J, et al.
The Distribution of Artificial Intelligence–Derived Retinal Cardiovascular Risk Scores and Conventional Risk Factors in Two Korean Health Screening Cohorts: A Descriptive Study.
Cardiovascular Prevention and Pharmacotherapy. 2025;7(3):73–84.
doi:10.36011/cpp.2025.7.e14.
研究分析逾 13.8 萬名受檢者，顯示 AI 視網膜心血管風險分數與年齡、傳統危險因子及冠狀動脈鈣化相關。
This study analysed more than 138,000 participants and found that AI-derived retinal cardiovascular scores were associated with age, conventional risk factors, and coronary artery calcium.
上述研究支持 AI 視網膜影像在糖尿病視網膜病變、腦健康及心血管風險篩查中的潛在用途。篩查結果不等同臨床診斷，應由醫護專業人員結合個人情況作出解讀。
These studies support the potential use of retinal AI for diabetic retinopathy, brain-health, and cardiovascular-risk screening. Screening results are not a clinical diagnosis and should be interpreted by qualified healthcare professionals in context.
