AIMC Topic: Machine Learning

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Machine learning for the prediction of pathologic pneumatosis intestinalis.

Surgery
BACKGROUND: The radiographic finding of pneumatosis intestinalis can indicate a spectrum of underlying processes ranging from a benign finding to a life-threatening condition. Although radiographic pneumatosis intestinalis is relatively common, there...

A through-focus scanning optical microscopy dimensional measurement method based on deep-learning classification model.

Journal of microscopy
Through-focus scanning optical microscopy (TSOM) is an economical, non-contact and nondestructive method for rapid measurement of three-dimensional nanostructures. There are two methods using TSOM image to measure the dimensions of one sample, includ...

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach.

Journal of biological physics
The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet ...

Independent real-world application of a clinical-grade automated prostate cancer detection system.

The Journal of pathology
Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists...

Machine-learning based prediction of Cushing's syndrome in dogs attending UK primary-care veterinary practice.

Scientific reports
Cushing's syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing's syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-...

Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times.

Computational intelligence and neuroscience
Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals' lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it ...

Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning.

Frontiers in immunology
A key hurdle to making adeno-associated virus (AAV) capsid mediated gene therapy broadly beneficial to all patients is overcoming pre-existing and therapy-induced immune responses to these vectors. Recent advances in high-throughput DNA synthesis, mu...

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN.

PloS one
Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especia...

Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA-lncRNA Interaction Prediction.

Interdisciplinary sciences, computational life sciences
MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are both non-coding RNAs (ncRNAs) and their interactions play important roles in biological processes. Computational methods, such as machine learning and various bioinformatics tools, can predict...

Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations.

Analytica chimica acta
The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impract...