AIMC Topic: Deep Learning

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MRI-based deep learning for differentiating between bipolar and major depressive disorders.

Psychiatry research. Neuroimaging
Mood disorders, particularly bipolar disorder (BD) and major depressive disorder (MDD), manifest changes in brain structure that can be detected using structural magnetic resonance imaging (MRI). Although structural MRI is a promising diagnostic tool...

Large-scale deep learning identifies the antiviral potential of PKI-179 and MTI-31 against coronaviruses.

Antiviral research
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the global pandemic of Coronavirus Disease (2019) (COVID-19), underscoring the urgency for effective antiviral drugs. Despite the development of different vaccination strategies,...

Personalized food consumption detection with deep learning and Inertial Measurement Unit sensor.

Computers in biology and medicine
For individuals diagnosed with diabetes mellitus, it is crucial to keep a record of the carbohydrates consumed during meals, as this should be done at least three times daily, amounting to an average of six meals. Unfortunately, many individuals tend...

Deep learning enabled in vitro predicting biological tissue thickness using force measurement device.

Computers in biology and medicine
Accurate perception of biological tissues (BT) thickness is essential for preliminary evaluation of medical diagnosis and animal nutrition. However, traditional thickness measuring approaches of BT require complex operation, high-cost, and trigger bi...

The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform.

Journal of chemical information and modeling
Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmaco...

Deep learning for blood glucose level prediction: How well do models generalize across different data sets?

PloS one
Deep learning-based models for predicting blood glucose levels in diabetic patients can facilitate proactive measures to prevent critical events and are essential for closed-loop control therapy systems. However, selecting appropriate models from the...

Performance of a Novel Muscle Synergy Approach for Continuous Motion Estimation on Untrained Motion.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
When applying continuous motion estimation (CME) model based on sEMG to human-robot system, it is inevitable to encounter scenarios in which the motions performed by the user are different from the motions in the training stage of the model. It has b...

Upfront surgery for intrahepatic cholangiocarcinoma: Prediction of futility using artificial intelligence.

Surgery
OBJECTIVE: We sought to identify patients at risk of "futile" surgery for intrahepatic cholangiocarcinoma using an artificial intelligence (AI)-based model based on preoperative variables.

Research on variety identification of common bean seeds based on hyperspectral and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Accurate, fast and non-destructive identification of varieties of common bean seeds is important for the cultivation and efficient utilization of common beans. This study is based on hyperspectral and deep learning to identify the varieties of common...

Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pat...