Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia

Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.

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Small Bowel Endoscopy Diagnostic Yield and Reasons of Obscure GI Bleeding in Chinese Patients

Gastroenterology Research and Practice ◽

10.1155/2014/437693 ◽

2014 ◽

Vol 2014 ◽

pp. 1-5

Author(s):

Ya-Fei He ◽

Ning-Bo Hao ◽

Wu-Chen Yang ◽

Li Yang ◽

Zhong-Li Liao ◽

...

Keyword(s):

Diagnostic Yield ◽

Vascular Lesion ◽

Chinese Patients ◽

Vascular Lesions ◽

Mass Lesion ◽

Positive Finding ◽

Age Group ◽

Double Balloon Enteroscopy ◽

Common Etiology ◽

Small Bowel Endoscopy

Aim. To investigate the diagnostic yield and etiologies of patients with obscure gastrointestinal bleeding (OGIB) using capsule endoscopy (CE) or double-balloon enteroscopy (DBE).Method. We studied the data of 532 consecutive patients with OGIB that were referred to Xinqiao Hospital in Chongqing from December 2005 to January 2012. A lesion that was believed to be the source of the bleeding (ulceration, mass lesion, vascular lesion, visible blood, inflammation, or others) was considered to be a positive finding. We analyzed the diagnostic yield of CE and SBE and the etiologies of OGIB.Result. CE and SBE have similar diagnostic yields, at 71.9% (196/231) and 71.8% (251/304), respectively. The most common etiology was erosions/ulceration (27.1%) followed by mass lesion (19.4%) and angiodysplastic/vascular lesions (13.9%). By stratified analysis, we found that erosions/ulceration (27.1%) was the most common etiology for the 21–40-year age group. Mass lesion was the most common etiology in the 41–60-year age group. However, in the >60 years age group, angiodysplastic/vascular lesions were significantly increased compared with the other groups, even though erosions/ulceration was most common.Conclusion. In this study, we found that CE and SBE have similar diagnostic yields and erosions/ulceration was the most common reason for OGIB, followed by mass lesion and angiodysplasias.

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Is It Possible to Predict the Presence of Intestinal Angioectasias?

Diagnostic and Therapeutic Endoscopy ◽

10.1155/2014/461602 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 7

Author(s):

Tiago Cúrdia Gonçalves ◽

Joana Magalhães ◽

Pedro Boal Carvalho ◽

Maria João Moreira ◽

Bruno Rosa ◽

...

Keyword(s):

Gastrointestinal Tract ◽

Small Bowel ◽

Gastrointestinal Bleeding ◽

Predictive Factors ◽

Capsule Endoscopy ◽

Diagnostic Tool ◽

Diagnostic Yield ◽

Vascular Lesions ◽

Important Diagnostic Tool ◽

The Mean

Background and Aim. Angioectasias are the most common vascular anomalies found in the gastrointestinal tract. In small bowel (SB), they can cause obscure gastrointestinal bleeding (OGIB) and in this setting, small bowel capsule endoscopy (SBCE) is an important diagnostic tool. This study aimed to identify predictive factors for the presence of SB angioectasias, detected by SBCE. Methods. We retrospectively analyzed the results of 284 consecutive SBCE procedures between April 2006 and December 2012, whose indication was OGIB, of which 47 cases with SB angioectasias and 53 controls without vascular lesions were selected to enter the study. Demographic and clinical data were collected. Results. The mean age of subjects with angioectasias (70.9±14.7) was significantly higher than in controls (53.1±18.6; P<0.001). The presence of SB angioectasias was significantly higher when the indication for the exam was overt OGIB versus occult OGIB (13/19 versus 34/81, P=0.044). Hypertension and hypercholesterolemia were significantly associated with the presence of SB angioectasias (38/62 versus 9/38, P<0.001 and 28/47 versus 19/53, P=0.027, resp.). Other studied factors were not associated with small bowel angioectasias. Conclusions. In patients with OGIB, overt bleeding, older age, hypercholesterolemia, and hypertension are predictive of the presence of SB angioectasias detected by SBCE, which may be used to increase the diagnostic yield of the SBCE procedure and to reduce the proportion of nondiagnostic examinations.

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Is Occult Obscure Gastrointestinal Bleeding a Definite Indication for Capsule Endoscopy? A Retrospective Analysis of Diagnostic Yield in Patients with Occult versus Overt Bleeding

Gastroenterology Research and Practice ◽

10.1155/2013/915463 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 10

Author(s):

Ikue Watari ◽

Shiro Oka ◽

Shinji Tanaka ◽

Makoto Nakano ◽

Taiki Aoyama ◽

...

Keyword(s):

Small Bowel ◽

Gastrointestinal Bleeding ◽

Capsule Endoscopy ◽

Diagnostic Yield ◽

Vascular Lesions ◽

Obscure Gastrointestinal Bleeding ◽

Detection Rates ◽

Significant Difference ◽

Bowel Diseases ◽

Small Bowel Diseases

Background/Aim. Usefulness of capsule endoscopy (CE) for diagnosing small-bowel lesions in patients with obscure gastrointestinal bleeding (OGIB) has been reported. Most reports have addressed the clinical features of overt OGIB, with few addressing occult OGIB. We aimed to clarify whether occult OGIB is a definite indication for CE.Methods. We retrospectively compared the cases of 102 patients with occult OGIB and 325 patients with overt OGIB, all having undergone CE. The diagnostic yield of CE and identification of various lesion types were determined in cases of occult OGIB versus overt OGIB.Results. There was no significant difference in diagnostic yield between occult and overt OGIB. The small-bowel lesions in cases of occult OGIB were diagnosed as ulcer/erosive lesions (n=18, 18%), vascular lesions (n=11, 11%), and tumors (n=4, 3%), and those in cases of overt OGIB were diagnosed as ulcer/erosive lesions (n=51, 16%), vascular lesions (n=31, 10%), and tumors (n=20, 6%).Conclusion. CE detection rates and CE identification of various small-bowel diseases do not differ between patients with occult versus overt OGIB. CE should be actively performed for patients with either occult or overt OGIB.

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DOES POLYETHYLENE GLYCOL CLEANSING PURGE IMPROVE VIDEO CAPSULE ENDOSCOPY DIAGNOSTIC YIELD IN OBSCURE GASTROINTESTINAL BLEEDING?

10.1055/s-0038-1637078 ◽

2018 ◽

Author(s):

F Cholet ◽

G Rahmi ◽

M Gaudric ◽

J Filippi ◽

C Duburque ◽

...

Keyword(s):

Polyethylene Glycol ◽

Gastrointestinal Bleeding ◽

Capsule Endoscopy ◽

Diagnostic Yield ◽

Video Capsule Endoscopy ◽

Obscure Gastrointestinal Bleeding

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Deep Learning-based Automatic Detection of Radiopaque Markers for Measuring Colon Transit Time

The Journal of Next-generation Convergence Technology Association ◽

10.33097/jncta.2020.04.04.361 ◽

2020 ◽

Vol 4 (4) ◽

pp. 361-366

Author(s):

Chansu Lee ◽

◽

Yehoon Jang ◽

Namgi Kim

Keyword(s):

Deep Learning ◽

Transit Time ◽

Automatic Detection ◽

Colon Transit Time ◽

Radiopaque Markers ◽

Colon Transit

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Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽

10.3390/s20061579 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1579

Author(s):

Dongqi Wang ◽

Qinghua Meng ◽

Dongming Chen ◽

Hupo Zhang ◽

Lisheng Xu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Channel Model ◽

Expert Knowledge ◽

Automatic Detection ◽

Data Representation ◽

Learning Technology ◽

Arrhythmia Detection ◽

Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

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Automatic detection of papilledema through fundus retinal images using deep learning

Microscopy Research and Technique ◽

10.1002/jemt.23865 ◽

2021 ◽

Author(s):

Tanzila Saba ◽

Shahzad Akbar ◽

Hoshang Kolivand ◽

Saeed Ali Bahaj

Keyword(s):

Deep Learning ◽

Automatic Detection ◽

Retinal Images

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Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

Scientific Reports ◽

10.1038/s41598-021-88236-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Christian Crouzet ◽

Gwangjin Jeong ◽

Rachel H. Chae ◽

Krystal T. LoPresti ◽

Cody E. Dunn ◽

...

Keyword(s):

Deep Learning ◽

Prussian Blue ◽

Processing Speed ◽

Digital Pathology ◽

Ground Truth ◽

Individual Variability ◽

Rgb Images ◽

Cerebral Microhemorrhages ◽

Phasor Analysis ◽

Better Than

AbstractCerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5–40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

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