AIMC Topic: Diagnosis, Computer-Assisted

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Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN.

International journal of computer assisted radiology and surgery
PURPOSE: Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Radiologists usually review knee X-ray images and grade the severity of the impairments according to the Kellgren-Lawrence grading scheme. However, this...

Deep Learning of Markov Model-Based Machines for Determination of Better Treatment Option Decisions for Infertile Women.

Reproductive sciences (Thousand Oaks, Calif.)
In this technical article, we are proposing ideas, that we have been developing on how machine learning and deep learning techniques can potentially assist obstetricians/gynecologists in better clinical decision-making, using infertile women in their...

Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.

Scientific reports
In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted by human readers. However, they are challenging to scale due to hardware costs and the dearth of pro...

Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy.

Journal of cardiovascular computed tomography
BACKGROUND: Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruc...

Multi-Modal Diagnosis of Infectious Diseases in the Developing World.

IEEE journal of biomedical and health informatics
In low and middle income countries, infectious diseases continue to have a significant impact, particularly amongst the poorest in society. Tetanus and hand foot and mouth disease (HFMD) are two such diseases and, in both, death is associated with au...

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.

The Lancet. Oncology
BACKGROUND: An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatm...

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study.

The Lancet. Oncology
BACKGROUND: The Gleason score is the strongest correlating predictor of recurrence for prostate cancer, but has substantial inter-observer variability, limiting its usefulness for individual patients. Specialised urological pathologists have greater ...

Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model.

Scientific reports
We aimed to develop a computer-aided diagnostic system (CAD) for predicting colorectal polyp histology using deep-learning technology and to validate its performance. Near-focus narrow-band imaging (NBI) pictures of colorectal polyps were retrieved f...

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods.

Computer methods and programs in biomedicine
Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing...