AIMC Topic: Diagnostic Errors
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Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis.
PloS one
Jun 11, 2020
BACKGROUND: Onychomycosis is the most common nail disorder and is associated with diagnostic challenges. Emerging non-invasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of thi...
Female
Dermoscopy
Young Adult
Neural Networks, Computer
Algorithms
Prospective Studies
Middle Aged
Diagnosis, Computer-Assisted
Deep Learning
Computer Systems
Adult
Diagnostic Errors
Male
Humans
Foot Dermatoses
Onychomycosis
Potassium Compounds
Mycological Typing Techniques
Photography
Predictive Value of Tests
Dermatologists
Hydroxides
The Need for a National Strategy on Artificial Intelligence in Canadian Dermatology.
Journal of cutaneous medicine and surgery
May 4, 2020
[Do artificial intelligence systems reason in the same way as clinicians when making diagnoses?].
La Revue de medecine interne
Jan 25, 2020
Clinical reasoning is at the heart of physicians' competence, as it allows them to make diagnoses. However, diagnostic errors are common, due to the existence of reasoning biases. Artificial intelligence is undergoing unprecedented development in thi...
MR-Forest: A Deep Decision Framework for False Positive Reduction in Pulmonary Nodule Detection.
IEEE journal of biomedical and health informatics
Oct 15, 2019
With the development of deep learning methods such as convolutional neural network (CNN), the accuracy of automated pulmonary nodule detection has been greatly improved. However, the high computational and storage costs of the large-scale network hav...
Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis.
Computational intelligence and neuroscience
Sep 26, 2019
This paper presents a fault detection and diagnosis (FDD) method, which uses one-dimensional convolutional neural network (1-D CNN) and WaveCluster clustering analysis to detect and diagnose sensor faults in the supply air temperature ( ) control loo...
Symptomatology differences of major depression in psychiatric versus general hospitals: A machine learning approach.
Journal of affective disorders
Sep 4, 2019
BACKGROUND: Symptomatology differences of major depressive disorder (MDD) in psychiatric and general hospitals in China leads to possible misdiagnosis. Looking at the symptomatology of first-visit patients with MDD in different mental health services...
Combining a gravitational search algorithm, particle swarm optimization, and fuzzy rules to improve the classification performance of a feed-forward neural network.
Computer methods and programs in biomedicine
Aug 8, 2019
BACKGROUND AND OBJECTIVE: A feed-forward neural network (FNN) is a type of artificial neural network that has been widely used in medical diagnosis, data mining, stock market analysis, and other fields. Many studies have used FNN to develop medical d...
Machine learning concepts, concerns and opportunities for a pediatric radiologist.
Pediatric radiology
Mar 29, 2019
Machine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and value of pediatric radiology. We describe specific types of learning, including supervised, unsupervis...
Artificial intelligence and colonoscopy: Current status and future perspectives.
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Feb 27, 2019
BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer-aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is s...
A Parametric Design Method for Optimal Quick Diagnostic Software.
Sensors (Basel, Switzerland)
Feb 21, 2019
Fault diagnostic software is required to respond to faults as early as possible in time-critical applications. However, the existing methods based on early diagnosis are not adequate. First, there is no common standard to quantify the response time o...
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