scholarly journals New endoscopic images of mucosal prolapse syndrome

Endoscopy ◽  
2009 ◽  
Vol 41 (S 02) ◽  
pp. E136-E136
Author(s):  
C. Chen ◽  
H. Isomoto ◽  
H. Ishii ◽  
T. Hayashi ◽  
Y. Mizuta ◽  
...  
Keyword(s):  
Mucosal Prolapse ◽  
Endoscopic Images

Hemorrhoidal Laser procedure (HeLP) and Hemorrrrhoidal Laser procedure + mucopexy (HeLPexx) and other Emerging Technologies

2020 ◽  
Vol 15 ◽  
Author(s):  
Paolo Giamundo
Keyword(s):  
Laser Treatment ◽  
Dentate Line ◽  
Mucosal Prolapse ◽  
Daily Activities ◽  
Common Goal ◽  
Superior Rectal Artery ◽  
Doppler Probe ◽  
The Common ◽  
Probe Set

Background: Minimally-invasive treatments for hemorrhoids should be encouraged as they cause low morbidity, reasonable discomfort and quicker return to work. According to the “vascular theory” hemorrhoidal disease is mainly caused by blood overflow into hemorrhoidal plexus deriving from the superior hemorrhoidal arteries. Introduction: Many different procedures have been described in the literature with the common goal of reducing the blood flow into the hemorrhoidal piles. ‘HeLP’ (Hemorrhoids Laser Procedure) is a novel form of dearterialization to treat patients suffering from symptomatic hemorrhoids. Methods: The procedure consists of the closure of the terminal branches of the superior rectal artery approximately 2-3 cm above the dentate line by means of laser shots originated by a diode laser platform. The arteries, at that level, have variable location and distribution. Therefore, a doppler probe set at the frequency of 20MHz helps identifying the arteries that would be missed otherwise. The laser beam is well tolerated by patients. For this reason, anesthesia is not required in most cases and the procedure allows a quick return to daily activities. In case of concomitant severe mucosal prolapse, the laser treatment can be combined with suture mucopexy. Three to six running sutures allow a complete lifting of hemorrhoidal piles, securing long-term resolution of symptoms. Results and Conclusions: ‘HeLP’ is indicated in patients with symptomatic hemorrhoids where conservative treatment failed and when mucosal prolapse is scarce or not symptomatic. The addition of mucopexy to laser treatment (HeLPexx) contributes to overall resolution of symptoms when mucosal prolapse is an issue, Emborrhoid is another novel, ‘hi-tech’ form of selective dearterialization used in selected case of hemorrhoids where main symptom is bleeding. It is generally used in cases where surgery is contraindicated due to severe concomitant diseases.

Gastroduodenal Mucosal Prolapse

2002 ◽  
Vol 178 (5) ◽  
pp. 1292-1293 ◽  
Author(s):  
Yvonne W. Lui ◽  
Emil J. Balthazar
Keyword(s):  
Mucosal Prolapse

scholarly journals Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jung Su Lee ◽  
Jihye Yun ◽  
Sungwon Ham ◽  
Hyunjung Park ◽  
Hyunsu Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Previous History ◽  
Clinical Factor ◽  
Endoscopic Images ◽  
Simplex Virus

AbstractThe endoscopic features between herpes simplex virus (HSV) and cytomegalovirus (CMV) esophagitis overlap significantly, and hence the differential diagnosis between HSV and CMV esophagitis is sometimes difficult. Therefore, we developed a machine-learning-based classifier to discriminate between CMV and HSV esophagitis. We analyzed 87 patients with HSV esophagitis and 63 patients with CMV esophagitis and developed a machine-learning-based artificial intelligence (AI) system using a total of 666 endoscopic images with HSV esophagitis and 416 endoscopic images with CMV esophagitis. In the five repeated five-fold cross-validations based on the hue–saturation–brightness color model, logistic regression with a least absolute shrinkage and selection operation showed the best performance (sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve: 100%, 100%, 100%, 100%, 100%, and 1.0, respectively). Previous history of transplantation was included in classifiers as a clinical factor; the lower the performance of these classifiers, the greater the effect of including this clinical factor. Our machine-learning-based AI system for differential diagnosis between HSV and CMV esophagitis showed high accuracy, which could help clinicians with diagnoses.

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽  
2020 ◽  
Author(s):  
Alanna Ebigbo ◽  
Robert Mendel ◽  
Tobias Rückert ◽  
Laurin Schuster ◽  
Andreas Probst ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pilot Study ◽  
White Light ◽  
Submucosal Invasion ◽  
Learning System ◽  
Endoscopic Images ◽  
Barrett’S Cancer ◽  
Significant Difference ◽  
Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

Sign in / Sign up

Export Citation Format

Share Document