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The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population.

Skeletal radiology
OBJECTIVE: Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray feat...

Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.

Breast (Edinburgh, Scotland)
In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate predicti...

[Expression of plasma microRNA in patients with acromegaly].

Problemy endokrinologii
BACKGROUND: microRNA is a class of small non-coding RNA molecules involved in posttranscriptional regulation of gene expression. MicroRNAs are detectable in blood in stable concentrations, which makes them promising biomarkers for various diseases.

Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy.

Ultrasound in medicine & biology
Knee arthroscopy is a minimally invasive surgery used in the treatment of intra-articular knee pathology which may cause unintended damage to femoral cartilage. An ultrasound (US)-guided autonomous robotic platform for knee arthroscopy can be envisio...

Prediction of lithium response using clinical data.

Acta psychiatrica Scandinavica
OBJECTIVE: Promptly establishing maintenance therapy could reduce morbidity and mortality in patients with bipolar disorder. Using a machine learning approach, we sought to evaluate whether lithium responsiveness (LR) is predictable using clinical ma...

Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results.

Medical physics
PURPOSE: Deep learning is emerging in radiology due to the increased computational capabilities available to reading rooms. These computational developments have the ability to mimic the radiologist and may allow for more accurate tissue characteriza...

ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.

EBioMedicine
BACKGROUND: The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". Ho...

Machine Learning Interpretation of Extended Human Papillomavirus Genotyping by Onclarity in an Asian Cervical Cancer Screening Population.

Journal of clinical microbiology
This study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay with the Cobas HPV test in identifying cervical intraepithelial neoplasia 2/3 or above (CIN2/3+) in an Asian screening population and (ii) to explore...

Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods.

BMC bioinformatics
BACKGROUND: Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evalua...