Chronic obstructive pulmonary disease (COPD) yields a high rate of failures such as hospital readmission and death in the United States, Canada and worldwide. COPD failure imposes a significant social and economic burden on society, and predicting su...
Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR genotype requires biopsy and sequence testing which is i...
OBJECTIVES: To retrospectively evaluate the diagnostic performance of a convolutional neural network (CNN) model in detecting pneumothorax on chest radiographs obtained after percutaneous transthoracic needle biopsy (PTNB) for pulmonary lesions.
Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor delineation to avoid toxicities from radiation therapy; however, tumor target contouring for head and neck...
BACKGROUND: Watson for oncology (WFO) is a cognitive computing system providing decision support. We evaluated the concordance rates between the treatment options determined by WFO and those determined by a multidisciplinary team (MDT).
OBJECTIVES: The present study aimed to compare the diagnostic performance of a machine learning (ML)-based FFR algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically s...
PURPOSE: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images.
Journal of laparoendoscopic & advanced surgical techniques. Part A
Mar 21, 2019
BACKGROUND: In the past 20 years, the fast spread of new surgical technologies has reached an important peak with the advent of the robotic surgery. Many studies have been run about a cosmetic desire to avoid neck scars after thyroid surgery and this...
PURPOSE: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound.
BACKGROUND: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing sarcoma-the second most common paediatric malignant bone tumour. Fourier transform infrared (FTIR) sp...
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