Diagnostic accuracy of a novel artificial intelligence system for adenoma detection in daily practice: a prospective non-randomized comparative study

Endoscopy ◽  
2021 ◽  
Author(s):  
Carolin Zippelius ◽  
Saleh A. Alqahtani ◽  
Jörg Schedel ◽  
Dominic Brookman-Amissah ◽  
Klaus Muehlenberg ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Comparative Study ◽  
Real Time ◽  
Interval Cancer ◽  
Daily Practice ◽  
Test Results ◽  
Intelligence System ◽  
Adenoma Detection ◽  
Significant Difference ◽  
Comparable Performance

Background and Aims: Adenoma detection rate (ADR) varies significantly between endoscopists with up to 26% adenoma miss rate (AMR). Artificial intelligence (AI) systems may improve endoscopic quality and reduce the rate of interval cancer. We evaluated the efficacy of an AI system in real time colonoscopy and its influence on the AMR and the ADR. Patients and methods: In this prospective non-randomized comparative study we analyzed 150 patients (age 65±14, 69 women, 81 men) undergoing diagnostic colonoscopy at a single endoscopy center in Germany from June to October 2020. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system GI Genius (Medtronic), overseen by a second observer, was not visible to the endoscopist. AMR was the primary outcome. Both methods were compared by the McNemar Test. Results: There was no significant and no clinically relevant difference (p=0.754) in AMR between the AI system (6/197, 3.0%, 95%CI [1.1-6.5]) and routine colonoscopy (4/197, 2.0%, 95%CI [0.6-5.1]). The polyp miss rate of the AI system (14/311, 4.5%, 95%CI [2.5-7.4]) was not significantly different (p=0.720) from routine colonoscopy (17/311, 5.5%, 95%CI [3.2-8.6]). There was no significant difference (p=0.500) between the ADR with routine colonoscopy (78/150, 52.0%, 95%CI [43.7-60.2]) and the AI system (76/150, 50.7%, 95%CI [42.4-58.9]). Routine colonoscopy detected adenomas in two patients that were missed by the AI system. Conclusion: The AI system had a comparable performance to experienced endoscopists during real-time colonoscopy with similar high ADR (>50%).

scholarly journals A Comparative Study of Mental Health among Old People

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Vandana N Solanki
Keyword(s):  
Mental Health ◽  
Comparative Study ◽  
Old Age ◽  
T Test ◽  
Test Results ◽  
Old People ◽  
Significant Difference ◽  
And Gender

The study was intended to examine the effect of mental health on old people. Aim: The aim was to estimate the prevalence of mental health in old people and to determine the association of mental health with types of family and gender. Sample: The sample consists of 120 old people from different old age home and family in Rajkot district area. The sample was selected from randomly. Design: 2*2research design was used the present study. Tools: Mental Health was measured through a questionnaire ‘Mental Health Inventory’was used. Test developed by Bhatt D & Gida G. in (1992).The data was analyzed by the t test. Results: There will be no significant difference between Gender and Types of Area in relation to their mental health. Conclusions: Our study demonstrates a higher prevalence of mental health in old people.

scholarly journals A Comparative Study of Anxiety among Diabetic Patients

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Vandana N Solanki
Keyword(s):  
Comparative Study ◽  
Research Design ◽  
T Test ◽  
Diabetic Patients ◽  
Test Results ◽  
Anxiety Test ◽  
Significant Difference ◽  
Patients With Diabetes ◽  
And Gender

The study was intended to examine the effect of Anxiety on diabetic patients. Aim: The aim was to estimate the prevalence of anxiety in patients with diabetes and to determine the association of anxiety with area and gender. Sample: The sample consists of 160 diabetic patients from different hospital in Rajkot district area. The sample was selected from randomly. Design: 2*2 research design was used the present study. Tools: Anxiety was measured through a questionnaire ‘Sinha’s Comprehensive Anxiety Test (SCAT) was used. Test developed by A.K.P Sinha and L.N.K Sinha in (1995).The data was analyzed by the t test. Results: There will be no significant difference between Gender and Types of Area in relation to their Anxiety. Conclusions: Our study demonstrates a higher prevalence of anxiety in diabetic patients. No factor was significantly associated with anxiety.

scholarly journals Clinical deployment and validation of a radiology artificial intelligence system for COVID-19

2020 ◽  
Author(s):  
Behrooz Hashemian ◽  
Aman Manchanda ◽  
Matthew D. Li ◽  
Parisa Farzam ◽  
Suma D. Dash ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Real Time ◽  
Learning Algorithm ◽  
Care Delivery ◽  
Rapid Development ◽  
Severe Disease ◽  
Healthcare Providers ◽  
Clinical Workflow ◽  
Intelligence System

Abstract The global COVID-19 pandemic has disrupted patient care delivery in healthcare systems world-wide. For healthcare providers to better allocate their resources and improve the care for patients with severe disease, it is valuable to be able to identify those patients with COVID-19 who are at higher risk for clinical complications. This may help to optimize clinical workflow and more efficiently allocate scarce medical resources. To this end, medical imaging shows great potential and artificial intelligence (AI) algorithms have been developed to assist in diagnosing and risk stratifying COVID-19 patients. However, despite the rapid development of numerous AI models, these models cannot be clinically useful unless they can be deployed in real-world environments in real-time on clinical data. Here, we propose an end-to-end AI hospital-deployment architecture for COVID-19 medical imaging algorithms in hospitals. We have successfully implemented this system at our institution and it has been used in prospective clinical validation of a deep learning algorithm potentially useful for triaging of patients with COVID-19. We demonstrate that many orchestration processes are required before AI inference can be performed on a radiology studies in real-time with the AI model being just one of the components that make up the AI deployment system. We also highlight that failure of any one of these processes can adversely affect the model's performance.

scholarly journals PSXI-2 A computer vision system for feed bunk management in beef cattle feedlot

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 389-390
Author(s):  
João R R Dorea ◽  
Sek Cheong
Keyword(s):  
Artificial Intelligence ◽  
Beef Cattle ◽  
Real Time ◽  
Vision System ◽  
Management Practice ◽  
Computing System ◽  
Intelligence System ◽  
Cloud Computing System ◽  
Model Training ◽  
Cattle Feedlot

Abstract Feed bunk scoring is a common management practice in feedlots. Usually, the bunk score is assigned visually by a trained person. However, the subjectivity of bunk scoring and inconsistency across bunk readers can result in excessive variation on feed delivery. Such variation can result on feed waste, sub-optimal animal performance, and increased incidence of metabolic disorders. The objective of this study was to develop an artificial intelligence system to perform bunk management in real-time. RGB-cameras were installed above the feed bunk in a commercial feedlot, and a total of 4,280 images were acquired, together with visual bunk scores with four categories: empty (no feed remaining), low (scattered feed remaining), medium (30–50% of feed remaining), and full (> 50% of feed remaining). Cattle behavior at the feed bunk was also classified into four classes: empty (no cattle at the feed bunk); low (< 30% bunk occupied); medium (30–70% feed bunk occupied); full (above 70% feed bunk occupied). The labeled images were then used for model training and a new set of 105 images were used for validation. A deep neural network (DNN) called ResNet was implemented to generate the predictions using a transfer learning with weights from the ImageNet dataset. A cloud computing system was developed to acquire, process and store images every 15 minutes, and implement real-time predictions of bunk score and cattle behavior. Prediction accuracies across bank score categories were: 81.8% (empty), 82.4% (low), 88.8% (medium), and 90% (full). For cattle behavior, accuracies were: 83.7% (empty), 66.6% (low), 71.4% (medium), and 86.6% (full). Combining feed bunk score and cattle behavior can provide an important decision-making tool to improve nutritional management in beef cattle feedlot. The use of artificial intelligence can allow the development of fully automated real-time systems to enhance livestock operations.

scholarly journals Guide Teaching System Based on Artificial Intelligence

2018 ◽  
Vol 13 (08) ◽  
pp. 90 ◽  
Author(s):  
Dali Luo
Keyword(s):  
Artificial Intelligence ◽  
System Theory ◽  
Expert System ◽  
Learning Effect ◽  
Learning Strategy ◽  
Test Results ◽  
Artificial Intelligence System ◽  
Teaching System ◽  
Intelligence System ◽  
Classified Students

To improve the development and deployment efficiency of the system, this paper combined the software system with Java and AI language Prolog to achieve the guide teaching system based on artificial intel-ligence (AI). The system creatively adopted the theory of artificial intelligence expert system, at the same time, built a Struts+Spring+Hibernate lightweight JavaEE framework. The coupling degree of each module in the system was greatly reduced to facilitate the expansion of future functions. Based on the development principle of the artificial intelligence expert system, the system diagnosed the learner's mastery of each point of knowledge. It classified students' learning effect and evaluated the knowledge points. Making full use of the learning state of students and combining it with artificial intelligence expert system theory, the system developed a suitable learning strategy to help students improve their learning with less efforts. In addition, the system took the forgetting rule of human brain into account, which periodically presented trainees’ knowledge points assessment and avoided students wasting time. The purpose was to help students improve their learning effect. Finally, the system was tested. The test results showed that the system is applicable and useful. It is concluded that the artificial intelligence system provides an example for the same method and has certain reference significance.

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