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Deep Learning Algorithm‑Based MRI Radiomics and Pathomics for Predicting Microsatellite Instability Status in Rectal Cancer: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate multimodal deep-learning models based on clinical variables, multiparametric MRI (mp-MRI) and hematoxylin and eosin (HE) stained pathology slides for predicting microsatellite instability (MSI) status...

Nested CNN architecture for three-dimensional dose distribution prediction in tomotherapy for prostate cancer.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
BACKGROUND: The hypothesis of changing network layers to increase the accuracy of dose distribution prediction, instead of expanding their dimensions, which requires complex calculations, has been considered in our study.

Prediction of growth and feed efficiency in mink using machine learning algorithms.

Animal : an international journal of animal bioscience
The feed efficiency (FE) expresses as the amount of feed required per unit of BW gain. Since feed cost is the major input cost in the mink industry, evaluating of FE is a crucial step for competitiveness of the mink industry. However, the FE measures...

An automated framework for pediatric hip surveillance and severity assessment using radiographs.

International journal of computer assisted radiology and surgery
PURPOSE: Hip dysplasia is the second most common orthopedic condition in children with cerebral palsy (CP) and may result in disability and pain. The migration percentage (MP) is a widely used metric in hip surveillance, calculated based on an anteri...

Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods.

Psychiatry research. Neuroimaging
RATIONALE AND OBJECTIVES: To explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine ...

Advancements in opportunistic intracranial aneurysm screening: The impact of a deep learning algorithm on radiologists' analysis of T2-weighted cranial MRI.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
(1) Background: Unruptured Intracranial Aneurysms (UIAs) are common blood vessel malformations, occurring in up to 3 % of healthy adults. Magnetic Resonance Angiography (MRA) is frequently used for the screening of UIAs due to its high resolution in ...

Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy.

Clinical imaging
OBJECTIVES: Alzheimer's disease (AD) is a common neurodegenerative disorder that primarily affects older individuals. Due to its high incidence, an accurate and efficient stratification system could greatly aid in the clinical diagnosis and prognosis...

Machine learning-based discrimination of unipolar depression and bipolar disorder with streamlined shortlist in adolescents of different ages.

Computers in biology and medicine
BACKGROUND: Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimination difficult and time-consuming. For adolescents with high disease prevalence, an efficient diagno...