A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study
CY Cheung et al, The Lancet Digital Health, September 30, 2022
12?949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia.
Artificial intelligence for detection of Alzheimer's disease: demonstration of real-world value is required to bridge the translational gap
CR Marshall et al, The Lancet Digital Health, September 30, 2022
The wide availability of retinal photography could, in principle, support detection of Alzheimer's disease at population level, allowing earlier access to support and treatment. This raises important questions that have yet to be resolved around what constitutes a timely diagnosis of Alzheimer's disease, and how effectively earlier detection improves quality of life, prognosis, and future health-care resource requirements.
Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation
KP Venkatesh et al, NPJ Digital Medicine, Sepetmber 22, 2022
Health digital twins are defined as virtual representations (“digital twin”) of patients (“physical twin”) that are generated from multimodal patient data, population data, and real-time updates on patient and environmental variables. With appropriate use, HDTs can model random perturbations on the digital twin to gain insight into the expected behavior of the physical twin—offering groundbreaking applications in precision medicine, clinical trials, and public health.
The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field
G Coorey et al, NPJ Digital Medicine, August 26, 2022
Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realizing this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimize treatment selection for the real-life patient.