scholarly journals Development and Validation of a Cbct-Based Artificial Intelligence System for Accurate Diagnoses of Dental Diseases

2021 ◽  
Author(s):  
Matvey Ezhov ◽  
Maxim Gusarev ◽  
Maria Golitsyna ◽  
Julian Yates ◽  
Evgeny Kushnerev ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Interpretation ◽  
Three Dimensional ◽  
Learning Approaches ◽  
Dental Imaging ◽  
Intelligence System ◽  
Dental Diseases ◽  
Time Required ◽  
Development And Validation

Abstract Cone-beam computed tomography (CBCT) in dental practice is becoming increasingly popular. However, the correct teeth identification, positioning and diagnosis based on CBCT can be tedious and challenging for the untrained eye. This is due to additional training, specific knowledge and time required for analysis and diagnosis. When compared to conventional dental imaging methods. In this study, we introduce a novel artificial intelligence (AI) system that facilitates analysis and diagnosis. This system is based on deep learning approaches that can localize teeth and define pathologies within three-dimensional CBCT scans. The study showed that the diagnostic performance of AI system image interpretation reaches and sometimes exceeds in comparison to clinician’s expertise. In this randomized cross-over trial we demonstrated a significant improvement of aided diagnostic accuracy for various dental diseases in comparison to a group of radiologists that made unaided decisions. AI can be used for both stand-alone CBCT interpretation and as a decision support system to improve quality of diagnostics and time efficiency.

330 DEVELOPMENT AND VALIDATION OF AN ARTIFICIAL INTELLIGENCE SYSTEM TO OPTIMIZE CLINICIAN REVIEW OF PATIENT RECORDS

2021 ◽  
Vol 160 (6) ◽  
pp. S-64-S-65
Author(s):  
Ethan A. Chi ◽  
Gordon Chi ◽  
Cheuk To Tsui ◽  
Yan Jiang ◽  
Karolin Jarr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Patient Records ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Development And Validation

MOOCs, Artificial Intelligence Systems, and the Dilemma of Tertiary Education in the 21st Century

2020 ◽  
pp. 14-27 ◽  
Author(s):  
Ikedinachi Ayodele Power Wogu ◽  
Morris Edogiawere ◽  
Jesse Oluwafemi Katende ◽  
Edith Awogu-Maduagwu ◽  
Charles Nathaniel Chukwuedo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Online Courses ◽  
Tertiary Education ◽  
Quality Of Education ◽  
Learning Approaches ◽  
Marxian Theory ◽  
Ex Post ◽  
Education Industry

Recent research on the application of artificial intelligence (AI) technology in the education industry for teaching and learning has stirred up a revolution via the use of platforms like the massive open online courses (MOOC) the likes of which the world have never seen before. Millions through this platform can now enroll online to get one form of education or the other. Many scholars, however, doubt the quality of education transmitted and acquired via these platforms; hence, some scholars describe the education gotten through this medium as artificial education. A situation that has resulted in a kind of revolution in the education industry described as education tsunami. The Marxian theory of alienation offers an appropriate theoretical platform for the analysis conducted in the paper. The ex-post factor method of analysis and Deidra's critical analytic method was adopted for attaining the objectives of the paper. The dilemmas eroding the quality of education were identified. Blended learning approaches, as against present methods, were recommended.

NIMG-55. AUGMENTED INTELLIGENCE IS SUPERIOR TO ARTIFICIAL INTELLIGENCE! HUMAN-COMPUTER SYNERGY FOR GENERATING HIGH QUALITY GLIOBLASTOMA SUB-REGION SEGMENTATIONS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi141-vi142
Author(s):  
Satyam Ghodasara ◽  
Ujjwal Baid ◽  
Spyridon Bakas ◽  
Michel Bilello ◽  
Suyash Mohan
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Methods ◽  
High Quality ◽  
Temporal Lobes ◽  
Signal Abnormality ◽  
Synergistic Approach ◽  
Mri Scans ◽  
Mri Sequences ◽  
Time Required

Abstract PURPOSE Artificial intelligence (AI) is poised to improve diagnostic methods in neuro-oncologic imaging and contribute to patient management by analyzing pre-operative MRI scans. AI results are better interpreted by compartmentalizing glioblastoma into distinct sub-regions, i.e., necrotic core, enhancing tumor, peritumoral T2/FLAIR signal abnormality (ED). Manual delineation of these sub-regions by expert neuroradiologists is impractical, requiring hours for intricate cases. Computer-aided segmentation (CAS) can mitigate this issue but is limited in the quality of the produced segmentations. We hypothesize that CAS followed by expert refinements is more practical/time-efficient. METHODS CAS was used on a total of 359 glioblastoma patients with four MRI sequences (T1, T1Gd, T2, T2-FLAIR) from each patient. All segmentations were sent to expert neuroradiologist annotators for manual refinements. Once refined, our team including two senior attending neuroradiologists with ≥13 years of experience each, reviewed and either approved or returned the segmentations to individual annotators for further refinements. Total time required to refine and review the finalized segmentations was measured. RESULTS Following one round of refinements by expert annotators, 244/359 (68%) segmentations were approved by our team while 115/359 (32%) segmentations contained a variety of errors that required a second round of refinements. The most common observed errors were 1) missed ED in the anterior/inferior temporal lobes and corpus callosum (37/115 cases, 32%) and 2) erroneous segmentation of normal choroid plexus and blood vessels (14/115 cases, 12%). The expert annotators required 120 hours to refine all 359 segmentations, and our team required 26 additional hours to review them, resulting in 24 minutes/segmentation following CAS. CONCLUSION Our findings support the value of a well-communicated annotation protocol to coordinate CAS and expert annotators. With CAS, our team and expert annotators rapidly finalized segmentations for 359 glioblastoma patients, demonstrating the value of a synergistic approach to creating high quality tumor sub-region segmentations.

scholarly journals Topical issues of legal regulation of artificial intelligence in the implementation of state and municipal service in the Russian Federation

2021 ◽  
Vol 27 (2) ◽  
pp. 160-163
Author(s):  
Ivan N. Melnikov ◽  
Ivan A. Samakov
Keyword(s):  
Artificial Intelligence ◽  
Russian Federation ◽  
Legal Regulation ◽  
Public Authorities ◽  
Municipal Service ◽  
The Public ◽  
Intelligence System ◽  
Artificial Intelligence Technology ◽  
The Russian Federation

This paper discusses the current issues of legal regulation in the field of artificial intelligence in the state and municipal service in the Russian Federation in order to ensure and protect the rights and freedoms of man and citizen. The article highlights the current problems that arise in the implementation of certain state functions, such as – the work of state bodies with citizens' appeals and the lack of regulatory regulation of the use of artificial intelligence technology in this process, the use of which will contribute to meeting the deadlines for working with citizens' appeals, as well as increase the overall level of quality of interaction between citizens and public authorities. Specific measures are proposed for the development of legislation in order to introduce artificial intelligence in solving the problems facing the public authorities. The article formulates the main conclusion regarding the trend of using the artificial intelligence system in the issue under consideration.

scholarly journals An Artificial Intelligence System to Predict Quality of Service in Banking Organizations

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Mauro Castelli ◽  
Luca Manzoni ◽  
Aleš Popovič
Keyword(s):  
Artificial Intelligence ◽  
Quality Of Service ◽  
Service Quality ◽  
Banking Sector ◽  
Traditional Approach ◽  
Analytical Models ◽  
Search Process ◽  
Artificial Intelligence System ◽  
Intelligence System

Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper, focusing on banking sector, we propose an artificial intelligence system for building a model for the prediction of service quality. While the traditional approach used for building analytical models relies on theories and assumptions about the problem at hand, we propose a novel approach for learning models from actual data. Thus, the proposed approach is not biased by the knowledge that experts may have about the problem, but it is completely based on the available data. The system is based on a recently defined variant of genetic programming that allows practitioners to include the concept of semantics in the search process. This will have beneficial effects on the search process and will produce analytical models that are based only on the data and not on domain-dependent knowledge.

Study of geometrical defects of free-form surface machined using neural network

2021 ◽  
Vol 13 (11) ◽  
pp. 168781402110609
Author(s):  
Benattia Bloul ◽  
Hélène Chanal ◽  
Benaoumeur Aour ◽  
Nargess Chtioui
Keyword(s):  
Artificial Intelligence ◽  
Three Dimensional ◽  
Free Form ◽  
Cad Model ◽  
3D Cad ◽  
Measuring Machine ◽  
The Difference ◽  
Geometrical Defects ◽  
Free Form Surfaces

The manufacture of total hip arthroplasty (THA) requires the control of the quality of free form surfaces. In fact, the polyethylene insert is deformed to fit the overall geometry of the femoral part, which has an impact on the quality of the contact. In this paper, we propose a method for evaluating the defects of complex forms. The originality of the approach is the use of artificial intelligence to position the cloud of measured points, obtained with a three-dimensional measuring machine equipped with a contactless sensor, with regard to the 3D CAD model of the THA. The artificial intelligence algorithm used is based on neural networks that are trained using a virtual positioning realized with 3D CAD software. Finally, the difference between the positioned point cloud and the CAD model allows us to evaluate the shape defect of the measured THA surface. We found that the error of the proposed method is at the vicinity of micron scale.

scholarly journals Intellectual systems in the management of medical technologies and quality of life

2021 ◽  
Vol 26 (1) ◽  
pp. 18-29
Author(s):  
S.O. Kizhaev ◽  
V.O. Petrenko ◽  
N.V. Mazur ◽  
V.V. Belitsky ◽  
А.V. Mazur ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Information Technologies ◽  
Clinical Medicine ◽  
Intelligent System ◽  
Technological Processes ◽  
Intelligence System ◽  
Artificial Intelligence Systems

The article is devoted to the development and use of intelligent systems in the management of medical technological processes and health-related quality of life (HRQOL). The relevance of the work is due to the need for effective use of intelligent systems in healthcare. The purpose of this work is to study the possibilities and prospects of using information technologies and artificial intelligence systems in clinical medicine to improve the efficiency of providing medical care to the population. Information retrieval method; theoretical analysis of legislative and regulatory documents, literary sources, Internet resources, research results; spectral-dynamic and mathematical analysis of the current state and assessment of the quality of life of an individual using the artificial intelligence system "CME". The paper analyzes the development trends of information technologies and artificial intelligence systems, as well as the features of their use in medical technological processes. As an example, the technological capabilities of the intelligent system Complex Medical Expert are briefly described.

scholarly journals Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Peng Xue ◽  
Chao Tang ◽  
Qing Li ◽  
Yuexiang Li ◽  
Yu Shen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic System ◽  
Clinical Information ◽  
Low Grade ◽  
High Grade ◽  
Diagnostic Quality ◽  
Screening Programs ◽  
Intelligence System ◽  
Validation Set ◽  
Development And Validation

Abstract Background Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies. Methods Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance. Results The agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516, p < 0.001). For detecting pathological high-grade squamous intraepithelial lesion or worse (HSIL+), CAIADS showed higher sensitivity than the use of colposcopies interpreted by colposcopists at either biopsy threshold (low-grade or worse 90.5%, 95% CI 88.9–91.4% versus 83.5%, 81.5–85.3%; high-grade or worse 71.9%, 69.5–74.2% versus 60.4%, 57.9–62.9%; all p < 0.001), whereas the specificities were similar (low-grade or worse 51.8%, 49.8–53.8% versus 52.0%, 50.0–54.1%; high-grade or worse 93.9%, 92.9–94.9% versus 94.9%, 93.9–95.7%; all p > 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758. Conclusions The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer.

scholarly journals Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records

2021 ◽  
Vol 4 (7) ◽  
pp. e2117391
Author(s):  
Ethan Andrew Chi ◽  
Gordon Chi ◽  
Cheuk To Tsui ◽  
Yan Jiang ◽  
Karolin Jarr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Patient Records ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Development And Validation
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