AIMC Topic: Workflow

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Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Molecular diversity
Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity a...

Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Molecular diversity
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data...

Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.

Radiation oncology (London, England)
PURPOSE: We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical ra...

De novo molecular design and generative models.

Drug discovery today
Molecular design strategies are integral to therapeutic progress in drug discovery. Computational approaches for de novo molecular design have been developed over the past three decades and, recently, thanks in part to advances in machine learning (M...

Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow.

Systematic reviews
BACKGROUND: Current text mining tools supporting abstract screening in systematic reviews are not widely used, in part because they lack sensitivity and precision. We set out to develop an accessible, semi-automated "workflow" to conduct abstract scr...

Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting...

AI-based pathology predicts origins for cancers of unknown primary.

Nature
Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primar...

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.

Nature protocols
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electron...

Against spatial-temporal discrepancy: contrastive learning-based network for surgical workflow recognition.

International journal of computer assisted radiology and surgery
PURPOSE: Automatic workflow recognition from surgical videos is fundamental and significant for developing context-aware systems in modern operating rooms. Although many approaches have been proposed to tackle challenges in this complex task, there a...

AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation.

Radiology
Background The workflow of breast cancer screening programs could be improved given the high workload and the high number of false-positive and false-negative assessments. Purpose To evaluate if using an artificial intelligence (AI) system could redu...