MP65-20 ON THE ROCKS: CAN UROLOGISTS IDENTIFY STONE COMPOSITION BASED ON ENDOSCOPIC IMAGES ALONE? A WORLDWIDE SURVEY OF UROLOGISTS

AbstractA total of 1520 patients with urinary stones from central China were collected and analysed by Fourier transform infrared spectroscopy between October 1, 2016 and December 31, 2019. For all patients, age, sex, comorbidities, stone location, laboratory examination and geographic region were collected. The most common stone component was calcium oxalate (77.5%), followed by calcium phosphate (8.7%), infection stone (7.6%), uric acid (UA) stone (5.3%)and cystine (0.9%). The males had more calcium oxalate stones (p < 0.001), while infection stone and cystine stones occurred more frequently in females (p < 0.001). The prevalence peak occurred at 41–60 years in both men and women. UA stones occurred frequently in patients with lower urinary pH (p < 0.001), while neutral urine or alkaline urine (p < 0.001) and urinary infection (p < 0.001) were more likely to be associated with infection stone stones. Patients with high levels of serum creatinine were more likely to develop UA stones (p < 0.001). The proportion of UA stones in diabetics was higher (p < 0.001), and the incidence of hypertension was higher in patients with UA stones (p < 0.001). Compared to the other types, more calcium oxalate stones were detected in the kidneys and ureters (p < 0.001), whereas struvite stones were more frequently observed in the lower urinary tract (p = 0.001). There was no significant difference in stone composition across the Qinling-Huaihe line in central China except UA stones, which were more frequently observed in patients south of the line (p < 0.001).

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

Scientific Reports ◽

10.1038/s41598-020-78556-z ◽

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.

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The Automatic Method of Endoscopic Images Analysis for Differential Diagnosis

2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus) ◽

10.1109/elconrus51938.2021.9396379 ◽

2021 ◽

Author(s):

Yang Xin ◽

Nataliia A. Obukhova ◽

Ivan P. Titov

Keyword(s):

Differential Diagnosis ◽

Automatic Method ◽

Endoscopic Images

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

Endoscopy ◽

10.1055/a-1311-8570 ◽

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.

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