Automatic Detection of Small Intestinal Hookworms in Capsule Endoscopy Images Based on a Convolutional Neural Network

Ancylostomiasis is a fairly common small bowel parasite disease identified by capsule endoscopy (CE) for which a computer-aided clinical detection method has not been established. We sought to develop an artificial intelligence system with a convolutional neural network (CNN) to automatically detect hookworms in CE images. We trained a deep CNN system based on a YOLO-V4 (You Look Only Once-Version4) detector using 11236 CE images of hookworms. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,529 small-bowel images including 531 images of hookworms. The trained CNN system required 403 seconds to evaluate 10,529 test images. The area under the curve for the detection of hookworms was 0.972 (95% confidence interval (CI), 0.967-0.978). The sensitivity, specificity, and accuracy of the CNN system were 92.2%, 91.1%, and 91.2%, respectively, at a probability score cut-off of 0.485. We developed and validated a CNN-based system for detecting hookworms in CE images. By combining this high-accuracy, high-speed, and oversight-preventing system with other CNN systems, we hope it will become an important supplement for detecting intestinal abnormalities in CE images. This trial is registered with ChiCTR2000034546 (a clinical research of artificial-intelligence-aided diagnosis for hookworms in small intestine by capsule endoscope images).

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

Digestive Endoscopy ◽

10.1111/den.13507 ◽

2020 ◽

Vol 32 (3) ◽

pp. 382-390 ◽

Cited By ~ 19

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

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S1357 Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Lymphangiectasia and Xanthomas Using a Convolutional Neural Network

The American Journal of Gastroenterology ◽

10.14309/01.ajg.0000778960.27312.84 ◽

2021 ◽

Vol 116 (1) ◽

pp. S624-S625

Author(s):

Tiago Ribeiro ◽

Miguel Mascarenhas ◽

Joao Afonso ◽

João Ferreira ◽

Hélder Cardoso ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Small Bowel ◽

Convolutional Neural Network ◽

Capsule Endoscopy ◽

Automatic Detection

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Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network

GE Portuguese Journal of Gastroenterology ◽

10.1159/000518901 ◽

2021 ◽

pp. 1-8

Author(s):

Miguel Mascarenhas Saraiva ◽

Tiago Ribeiro ◽

João Afonso ◽

João P.S. Ferreira ◽

Hélder Cardoso ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Small Bowel ◽

Convolutional Neural Network ◽

Capsule Endoscopy ◽

Automatic Detection ◽

Training Dataset ◽

Validation Dataset ◽

Accuracy And Precision ◽

Total Pool

Introduction: Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams. Methods: A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision. Results: Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s). Discussion/Conclusion: We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.

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ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure

European Heart Journal - Digital Health ◽

10.1093/ehjdh/ztab080 ◽

2021 ◽

Author(s):

Oguz Akbilgic ◽

Liam Butler ◽

Ibrahim Karabayir ◽

Patricia P Chang ◽

Dalane W Kitzman ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Heart Failure ◽

Convolutional Neural Network ◽

Prediction Models ◽

Smoking Status ◽

Characteristic Curve ◽

Gradient Boosting ◽

Model Output ◽

Light Gradient

Abstract Aims Heart failure (HF) is a leading cause of death. Early intervention is the key to reduce HF-related morbidity and mortality. This study assesses the utility of electrocardiograms (ECGs) in HF risk prediction. Methods and results Data from the baseline visits (1987–89) of the Atherosclerosis Risk in Communities (ARIC) study was used. Incident hospitalized HF events were ascertained by ICD codes. Participants with good quality baseline ECGs were included. Participants with prevalent HF were excluded. ECG-artificial intelligence (AI) model to predict HF was created as a deep residual convolutional neural network (CNN) utilizing standard 12-lead ECG. The area under the receiver operating characteristic curve (AUC) was used to evaluate prediction models including (CNN), light gradient boosting machines (LGBM), and Cox proportional hazards regression. A total of 14 613 (45% male, 73% of white, mean age ± standard deviation of 54 ± 5) participants were eligible. A total of 803 (5.5%) participants developed HF within 10 years from baseline. Convolutional neural network utilizing solely ECG achieved an AUC of 0.756 (0.717–0.795) on the hold-out test data. ARIC and Framingham Heart Study (FHS) HF risk calculators yielded AUC of 0.802 (0.750–0.850) and 0.780 (0.740–0.830). The highest AUC of 0.818 (0.778–0.859) was obtained when ECG-AI model output, age, gender, race, body mass index, smoking status, prevalent coronary heart disease, diabetes mellitus, systolic blood pressure, and heart rate were used as predictors of HF within LGBM. The ECG-AI model output was the most important predictor of HF. Conclusions ECG-AI model based solely on information extracted from ECG independently predicts HF with accuracy comparable to existing FHS and ARIC risk calculators.

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403 A HIGHLY SENSITIVE AND HIGHLY SPECIFIC CONVOLUTIONAL NEURAL NETWORK-BASED ALGORITHM FOR AUTOMATED DIAGNOSIS OF ANGIODYSPLASIA IN SMALL BOWEL CAPSULE ENDOSCOPY

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2018.04.071 ◽

2018 ◽

Vol 87 (6) ◽

pp. AB78

Author(s):

Romain Leenhardt ◽

Pauline Vasseur ◽

Cynthia Li ◽

Gabriel Rahmi ◽

Franck Cholet ◽

...

Keyword(s):

Neural Network ◽

Small Bowel ◽

Convolutional Neural Network ◽

Capsule Endoscopy ◽

Automated Diagnosis ◽

Highly Sensitive

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Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer

Chinese Medical Journal ◽

10.1097/cm9.0000000000000095 ◽

2019 ◽

Vol 132 (4) ◽

pp. 379-387 ◽

Cited By ~ 2

Author(s):

Lei Ding ◽

Guang-Wei Liu ◽

Bao-Chun Zhao ◽

Yun-Peng Zhou ◽

Shuai Li ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Rectal Cancer ◽

Lymph Nodes ◽

Convolutional Neural Network ◽

Artificial Intelligence System ◽

Metastatic Lymph Nodes ◽

Intelligence System ◽

Metastatic Lymph

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Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network

Endoscopy ◽

10.1055/a-1167-8157 ◽

2020 ◽

Vol 52 (09) ◽

pp. 786-791 ◽

Cited By ~ 1

Author(s):

Keita Otani ◽

Ayako Nakada ◽

Yusuke Kurose ◽

Ryota Niikura ◽

Atsuo Yamada ◽

...

Keyword(s):

Neural Network ◽

Small Bowel ◽

Capsule Endoscopy ◽

Characteristic Curve ◽

Vascular Lesions ◽

Single Type ◽

Data Set ◽

Normal Tissues ◽

Computer Aided ◽

The Mean

Abstract Background Previous computer-aided detection systems for diagnosing lesions in images from wireless capsule endoscopy (WCE) have been limited to a single type of small-bowel lesion. We developed a new artificial intelligence (AI) system able to diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. Methods We trained the deep neural network system RetinaNet on a data set of 167 patients, which consisted of images of 398 erosions and ulcers, 538 vascular lesions, 4590 tumors, and 34 437 normal tissues. We calculated the mean area under the receiver operating characteristic curve (AUC) for each lesion type using five-fold stratified cross-validation. Results The mean age of the patients was 63.6 years; 92 were men. The mean AUCs of the AI system were 0.996 (95 %CI 0.992 – 0.999) for erosions and ulcers, 0.950 (95 %CI 0.923 – 0.978) for vascular lesions, and 0.950 (95 %CI 0.913 – 0.988) for tumors. Conclusion We developed and validated a new computer-aided diagnosis system for multiclass diagnosis of small-bowel lesions in WCE images.

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