… at baseline, were non-diabetic, had an LDL cholesterol measurement below 190 mg/dL,
and had a blood pressure measurement within the year prior to the ECG. These criteria were …

… diabetes greatly inhibits the progression of the disease. This study proposes a new method
based on deep learning for the early detection of diabetes. … in early diabetes diagnosis. Three …

… of DLM-enabled ECG to use personal pre-annotated ECGs to … Electrocardiography (ECG)
is commonly used to diagnose … Recently, deep learning model (DLM)-enabled ECG systems …

… For instance, Google’s algorithm for diagnosis of diabetic retinopathy performed poorly in
populations in India outside where the model had been developed. Other dramatic examples …

… ECG data, the system regarded 245 persons in which 160 volunteers are non-diabetic and
85 volunteers are diabetic… of diabetic retinopathy using PCA-firefly based deep learning model…

… ECG clustering techniques developed mainly in the last decade. The focus will be on recent
machine learning and deep learning … association between diabetes and ECG. However, …

… We pre-train an ECG-based deep learning model with identity identification and fine-tune
it … , such as echocardiographic results, diabetes mellitus markers, and hyperlipidemia-related …

… detection of diabetes are … of deep learning is the long training phase. We try to overcome
this limitation and offer a concept for classifying type 2 diabetes (T2D) using a machine learning …

… Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous
… In the present study, we trained a DNN to use ECGs to predict new-onset AF in patients with …

… ‐to‐end deep neural network can … ECG s. When further fine‐tuned with other clinical
outcomes and externally validated in clinical practice, the demonstrated deep learning–based ECG …