AIMC Topic: Deep Learning

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Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer's disease diagnosis and biomarker identification.

Artificial cells, nanomedicine, and biotechnology
The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multi...

End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study.

European radiology
OBJECTIVES: Pancreatic cancer treatment plans involving surgery and/or chemotherapy are highly dependent on disease stage. However, current staging systems are ineffective and poorly correlated with survival outcomes. We investigate how artificial in...

Deep learning-based detection of bacterial swarm motion using a single image.

Gut microbes
Motility is a fundamental characteristic of bacteria. Distinguishing between swarming and swimming, the two principal forms of bacterial movement, holds significant conceptual and clinical relevance. Conventionally, the detection of bacterial swarmin...

Development of a novel machine learning model to automate vertebral column segmentation utilizing biplanar full-body imaging.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Degenerative scoliosis (DS) is a common spinal disorder among adults, characterized by lateral curvature of the spine. Recent advancements in biplanar full-body imaging, a low-dose and weight-bearing X-ray modality, facilitate saf...

Leveraging multi-modal feature learning for predictions of antibody viscosity.

mAbs
The shift toward subcutaneous administration for biologic therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced healthcare burden, and improved compliance compared to traditional intravenous infusions. However, a s...

Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management.

Annals of medicine
Atopic dermatitis (AD) is a common and complex skin disease that significantly affects the quality of life of patients. The latest advances in artificial intelligence (AI) technology have introduced new methods for diagnosing, treating, and managing ...

Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning.

mAbs
Highly concentrated antibody solutions are necessary for developing subcutaneous injections but often exhibit high viscosities, posing challenges in antibody-drug development, manufacturing, and administration. Previous computational models were only...

Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: an image-deep-learning study.

Annals of medicine
OBJECTIVE: Asherman's syndrome (AS) is a significant cause of subfertility in women from developing countries. Over 80% of AS cases in these regions are linked to dilation and curettage (D&C) procedures following pregnancy. The incidence of AS in pat...

Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study.

Computer assisted surgery (Abingdon, England)
This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization laboratory (cath lab). We employed only the clinical phases derived from video analysis as input to ...