Pathophysiology, diagnosis and treatment of food protein-induced gastrointestinal diseases

2004 ◽  
Vol 4 (3) ◽  
pp. 221-229 ◽  
Author(s):  
Ralf G Heine
Keyword(s):  
Gastrointestinal Diseases ◽  
Diagnosis And Treatment ◽  
Food Protein

Food protein-induced enterocolitis syndrome epidemiology, diagnosis, and treatment

2020 ◽  
Vol 20 (3) ◽  
pp. 316-322 ◽  
Author(s):  
Valentina Pecora ◽  
Rocco Valluzzi ◽  
Lamia Dahdah ◽  
Vincenzo Fierro ◽  
Maurizio Mennini ◽  
...  
Keyword(s):  
Diagnosis And Treatment ◽  
Food Protein

scholarly journals Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation

10.2196/18563 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e18563
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Tahir Mahmood ◽  
Jin Kyu Kang ◽  
Kang Ryoung Park
Keyword(s):  
Deep Learning ◽  
Medical Condition ◽  
Early Stage ◽  
Gastrointestinal Diseases ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Computer Prediction ◽  
Gastrointestinal Lesions ◽  
Computer Aided ◽  
Aided Diagnosis

Background The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. Objective This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases. Methods Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment. Results All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods. Conclusions This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.

scholarly journals Exosomes: A new frontier under the spotlight for diagnosis and treatment of gastrointestinal diseases

2021 ◽  
Vol 9 (1) ◽  
pp. 12-28
Author(s):  
Maliha Naseer ◽  
Syeda Hadi ◽  
Ali Syed ◽  
Amer Safdari ◽  
Veysel Tahan
Keyword(s):  
Gastrointestinal Diseases ◽  
Diagnosis And Treatment ◽  
New Frontier

scholarly journals Analysis of digestive endoscopic results during COVID-19

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kun Huang ◽  
Xiaolin Zhao ◽  
Xianyan Chen ◽  
Yinghui Gao ◽  
Jiufei Yu ◽  
...  
Keyword(s):  
Detection Rate ◽  
Gastrointestinal Diseases ◽  
Diagnosis And Treatment ◽  
Antibody Detection ◽  
Civil Aviation ◽  
Nucleic Acid Detection ◽  
Control Group ◽  
Detection Rates ◽  
Epidemic Period ◽  
Novel Coronavirus

AbstractObjectiveThis study aims to explore the necessity and safety of digestive endoscopy during the epidemic of coronavirus disease 2019.MethodsA retrospective cohort study method was used to collect patients’ data from the endoscopy center of the Civil Aviation General Hospital of China from February 1 to May 31, 2020, as the observation group. The patients’ data of endoscopic diagnosis and treatment during the same period in 2019 were used as a control group, to compare the differences in the number of diagnosis and treatment and the detection rate of gastrointestinal diseases in the two groups. At the same time, patients and related staff were followed up for the situation of new infection.ResultsDuring the epidemic, our endoscopy center conducted a total of 1,808 cases of endoscopic operations and 5,903 cases in the control group. The amount of endoscopic work during the epidemic period was 30.63% in the same period last year. During the epidemic, 26 patients underwent endoscopic mucosal resection (EMR)/endoscopic submucosal dissection (ESD) treatment, 26 patients underwent ERCP, and 18 patients underwent gastrointestinal stent implantation. In the control group, 273 patients underwent EMR/ESD, 17 underwent ERCP, and 16 underwent gastrointestinal stenting. During COVID-19, compared with the same period last year, the detection rates of peptic ulcer, esophageal cancer, gastric cancer, colon cancer, and rectal cancer were significantly higher (χ2 = 4.482, P = 0.034; χ2 = 5.223, P = 0.006; χ2 = 2.329, P = 0.041; χ2 = 8.755, P = 0.003; and χ2 = 5.136, P = 0.023). Through telephone follow-up, novel coronavirus nucleic acid detection and blood antibody detection, no patients or medical staff were infected with the novel coronavirus.ConclusionDuring COVID-19, the number of digestive endoscopic operations decreased significantly compared with the same period last year, but the detection rate of various diseases of the digestive tract increased significantly. On the basis of strict prevention and control, orderly recovery of endoscopic work is essential.

scholarly journals Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Tahir Mahmood ◽  
Jin Kyu Kang ◽  
Kang Ryoung Park
Keyword(s):  
Deep Learning ◽  
Medical Condition ◽  
Early Stage ◽  
Gastrointestinal Diseases ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Computer Prediction ◽  
Gastrointestinal Lesions ◽  
Computer Aided ◽  
Aided Diagnosis

BACKGROUND The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. OBJECTIVE This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases. METHODS Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment. RESULTS All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods. CONCLUSIONS This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.

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