scholarly journals Development of an artificial intelligence model using an automatic detection of furcation involvement through panoramic radiography

2021 ◽  
Vol 63 (3) ◽  
pp. 119-128
Author(s):  
Satoshi Tajima ◽  
Chikanobu Sonoda ◽  
Takashi Kobayashi
Keyword(s):  
Artificial Intelligence ◽  
Panoramic Radiography ◽  
Automatic Detection ◽  
Furcation Involvement

ID: 3525204 ARTIFICIAL INTELLIGENCE AND VIDEO CAPSULE COLONOSCOPY: AUTOMATIC DETECTION OF BLOOD IN COLONIC LUMEN USING A CONVOLUCIONAL NEURAL NETWORK

2021 ◽  
Vol 93 (6) ◽  
pp. AB190-AB191
Author(s):  
João Afonso ◽  
Miguel M. Saraiva ◽  
Helder Cardoso ◽  
João Ferreira ◽  
Patrícia Andrade ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Automatic Detection ◽  
Colonic Lumen

Artificial intelligence using a convolutional neural network for automatic detection of small‐bowel angioectasia in capsule endoscopy images

2020 ◽  
Vol 32 (3) ◽  
pp. 382-390 ◽  
Author(s):  
Akiyoshi Tsuboi ◽  
Shiro Oka ◽  
Kazuharu Aoyama ◽  
Hiroaki Saito ◽  
Tomonori Aoki ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Small Bowel ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Automatic Detection

scholarly journals Automatic detection of mycobacterium tuberculosis using artificial intelligence

2018 ◽  
Vol 10 (3) ◽  
pp. 1936-1940 ◽  
Author(s):  
Yan Xiong ◽  
Xiaojun Ba ◽  
Ao Hou ◽  
Kaiwen Zhang ◽  
Longsen Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mycobacterium Tuberculosis ◽  
Automatic Detection

scholarly journals P188 Artificial Intelligence and Colon Capsule Endoscopy: Automatic detection of colonic mucosal lesions and blood using a convolutional neural network

2021 ◽  
Vol 15 (Supplement_1) ◽  
pp. S257-S258
Author(s):  
J Afonso ◽  
M Mascarenhas ◽  
T Ribeiro ◽  
H Cardoso ◽  
J Ferreira ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Operating Characteristic ◽  
Automatic Detection ◽  
Diagnostic Error ◽  
Normal Mucosa ◽  
Colon Capsule Endoscopy ◽  
Colon Capsule ◽  
Mucosal Lesions

Abstract Background Conventional colonoscopy is the standard criterion for the diagnosis and staging of colonic disease, including inflammatory bowel disease (IBD). However, it has the risk of complications, including bleeding and perforation. Colon capsule endoscopy (CCE) is a minimally invasive alternative for patients refusing conventional colonoscopy or for whom the latter is contraindicated. However, reading CCE images is a time-consuming task and prone to diagnostic error. Detection of colonic blood is relevant for the diagnosis and assessment of the activity of IBD, particularly ulcerative colitis. An accurate and early diagnosis is essential as it directs subsequent treatment. Our aim was to develop an Artificial Intelligence (AI) algorithm, based on a multilayer Convolutional Neural Network (CNN), for automatic detection of blood and other significant lesions in the colonic lumen in CCE exams: Methods A total of 24 CCE exams (PillCam COLON 2®) from a single centre, performed between 2010–2020, were analysed. From these exams 7640 images (2915 normal mucosa, 3065 blood and 1660 mucosal lesions) were ultimately extracted. Two image datasets were created for CNN training and testing. These images were inserted in a CNN model with transfer of learning. The output provided by the CNN was compared to the classification provided by a consensus of specialists (Figure 1). Performance marks included sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively), accuracy and area under the receiving operator characteristics curve (AUROC). Results Blood was detected with a sensitivity and specificity of 97.2% and 99.9%, respectively. The AUROC for detection of blood was 1.00 (Figure 2). Detection of other findings had a sensitivity and specificity of 83.7% and 96.7%, respectively. The overall accuracy of the CNN was of 95.4%. Figure 1 - Output obtained from the application of the convolutional neural network. N: normal mucosa; B - blood or hematic residues; ML – mucosal lesions. Figure 2 – Receiver operating characteristic analyses of the network’s performance in the detection of normal mucosa, blood and colon mucosal lesions. AUROC – are under the receiver operating characteristic curve; N – normal colonic mucosa; B – blood; ML – mucosal lesions. Conclusion We developed a pioneer CNN for CCE which demonstrated high levels of efficiency for the automatic detection of blood and lesions with high clinical significance. The development of tools for automatic detection of these lesions may allow for minimization of diagnostic error and the time spent evaluating these exams.

scholarly journals Detección automática de la Fibrilación Auricular a través de un Smartwatch

2019 ◽  
Vol 11 (4) ◽  
pp. 3
Author(s):  
Anna Abad Torrent ◽  
Helena Benito Naverac
Keyword(s):  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Clinical Practice ◽  
General Population ◽  
Cardiac Arrhythmia ◽  
Automatic Detection ◽  
Insuficiencia Cardiaca ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Electrocardiogram Ecg

La fibrilación auricular es la arritmia cardiaca más frecuente en la práctica clínica. La prevalencia se sitúa en torno al 0,4 - 1 % de la población general. Aumenta con la edad, llegando hasta el 8% a partir de los 80 años. Esta arritmia es la principal causa a nivel mundial de accidente cerebrovascular (20-30% de los casos son debidos a la fibrilación auricular), insuficiencia cardíaca o muerte súbita. Muchas veces , es clínicamente silente o se manifiesta con síntomas vagos como las palpitaciones, que pueden atribuirse erróneamente a ansiedad y retrasar el diagnóstico. La instauración temprana de anticoagulación (en determinados casos) reduce, de forma significativa la incidencia de fenómenos tromboembólicos. ABSTRACT Automatic detection of atrial fibrillation using a Smartwatch Atrial fibrillation is the most common cardiac arrhythmia in clinical practice. The prevalence is around 0.4 — 1% of the general population. It increases with age, reaching up to 8% from 80 years. In cardiology, the standard for the diagnosis of a cardiac arrhythmia is based on the performance of an electrocardiogram (ECG). From the monitoring of KardiaBand ™ and SmartRhythm ™, AliveCor launches the first platform for Apple Watch series 4, which combines an electrocardiography device approved by the FDA and certain analysis algorithms with artificial intelligence models, which help to detect the atrial fibrillation.  

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