AIMC Topic: Machine Learning

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Active learning for prediction of tensile properties for material extrusion additive manufacturing.

Scientific reports
Machine learning techniques were used to predict tensile properties of material extrusion-based additively manufactured parts made with Technomelt PA 6910, a hot melt adhesive. An adaptive data generation technique, specifically an active learning pr...

Systematical analysis of underlying markers associated with Marfan syndrome via integrated bioinformatics and machine learning strategies.

Journal of biomolecular structure & dynamics
Marfan syndrome (MFS) is a hereditary disease with high mortality. This study aimed to explore peripheral blood potential markers and underlying mechanisms in MFS via a series bioinformatics and machine learning analysis. First, we downloaded two MFS...

An unsupervised two-step training framework for low-dose computed tomography denoising.

Medical physics
BACKGROUND: Although low-dose computed tomography (CT) imaging has been more widely adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT images tend to have more noise, which impedes accurate diagnosis. Recently...

Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Optical Coherence Tomography (OCT) is an emerging technology that provides three-dimensional images of the microanatomy of biological tissue in-vivo and at micrometer-scale resolution. OCT imaging has been widely used to diagnose and manage various m...

Combination of deep learning and ensemble machine learning using intraoperative video images strongly predicts recovery of urinary continence after robot-assisted radical prostatectomy.

Cancer reports (Hoboken, N.J.)
BACKGROUND: We recently reported the importance of deep learning (DL) of pelvic magnetic resonance imaging in predicting the degree of urinary incontinence (UI) following robot-assisted radical prostatectomy (RARP). However, our results were limited ...

Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics.

Human genomics
BACKGROUND: This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have t...

Machine Learning Classification of Body Part, Imaging Axis, and Intravenous Contrast Enhancement on CT Imaging.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
The development and evaluation of machine learning models that automatically identify the body part(s) imaged, axis of imaging, and the presence of intravenous contrast material of a CT series of images. This retrospective study included 6955 serie...

External validation of machine learning algorithm predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients using a Taiwanese cohort.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND/PURPOSE: Identifying patients at risk of prolonged opioid use after surgery prompts appropriate prescription and personalized treatment plans. The Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) was developed to pred...

An Acoustical and Lexical Machine-Learning Pipeline to Identify .

Journal of palliative medicine
Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. To assess the feasibility of automating the identification of a conversational feature, which is associated with imp...

Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to addres...