AIMC Topic: Sensitivity and Specificity

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Using chatbots to screen for heritable cancer syndromes in patients undergoing routine colonoscopy.

Journal of medical genetics
BACKGROUND: Hereditary colorectal cancer (HCRC) syndromes account for 10% of colorectal cancers but remain underdiagnosed. This feasibility project tested the utility of an artificial intelligence-based chatbot deployed to patients scheduled for colo...

A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography.

International journal of computer assisted radiology and surgery
PURPOSE: Deep learning can be used for improving the performance of computer-aided detection (CADe) in various medical imaging tasks. However, in computed tomographic (CT) colonography, the performance is limited by the relatively small size and the ...

Reliability of robotic transcranial magnetic stimulation motor mapping.

Journal of neurophysiology
Robotic transcranial magnetic stimulation (TMS) is a noninvasive and safe tool that produces cortical motor maps using neuronavigational and neuroanatomical images. Motor maps are individualized representations of the primary motor cortex (M1) topogr...

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.

European journal of radiology
INTRODUCTION: Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and ...

Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Radiology
Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral a...

Predicting vaginal birth after previous cesarean: Using machine-learning models and a population-based cohort in Sweden.

Acta obstetricia et gynecologica Scandinavica
INTRODUCTION: Predicting a woman's probability of vaginal birth after cesarean could facilitate the antenatal decision-making process. Having a previous vaginal birth strongly predicts vaginal birth after cesarean. Delivery outcome in women with only...

The detection of lung cancer using massive artificial neural network based on soft tissue technique.

BMC medical informatics and decision making
BACKGROUND: A proposed computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer while a proposed CAD scheme recognizes non subtl...

Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors.

Korean journal of radiology
OBJECTIVE: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on d...

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.

Clinical chemistry and laboratory medicine
OBJECTIVES: The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expen...