scholarly journals Development of an artificial intelligence-based algorithm to classify images acquired with an intraoral scanner of individual molar teeth into three categories

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261870
Author(s):  
Nozomi Eto ◽  
Junichi Yamazoe ◽  
Akiko Tsuji ◽  
Naohisa Wada ◽  
Noriaki Ikeda
Keyword(s):  
Artificial Intelligence ◽  
Oral Cavity ◽  
Personal Identification ◽  
Automated System ◽  
Forensic Dentistry ◽  
Intraoral Scanner ◽  
Intraoral Scanning ◽  
Major Disaster ◽  
Molar Teeth ◽  
F Measure

Background Forensic dentistry identifies deceased individuals by comparing postmortem dental charts, oral-cavity pictures and dental X-ray images with antemortem records. However, conventional forensic dentistry methods are time-consuming and thus unable to rapidly identify large numbers of victims following a large-scale disaster. Objective Our goal is to automate the dental filing process by using intraoral scanner images. In this study, we generated and evaluated an artificial intelligence-based algorithm that classified images of individual molar teeth into three categories: (1) full metallic crown (FMC); (2) partial metallic restoration (In); or (3) sound tooth, carious tooth or non-metallic restoration (CNMR). Methods A pre-trained model was created using oral-cavity pictures from patients. Then, the algorithm was generated through transfer learning and training with images acquired from cadavers by intraoral scanning. Cross-validation was performed to reduce bias. The ability of the model to classify molar teeth into the three categories (FMC, In or CNMR) was evaluated using four criteria: precision, recall, F-measure and overall accuracy. Results The average value (variance) was 0.952 (0.000140) for recall, 0.957 (0.0000614) for precision, 0.952 (0.000145) for F-measure, and 0.952 (0.000142) for overall accuracy when the algorithm was used to classify images of molar teeth acquired from cadavers by intraoral scanning. Conclusion We have created an artificial intelligence-based algorithm that analyzes images acquired with an intraoral scanner and classifies molar teeth into one of three types (FMC, In or CNMR) based on the presence/absence of metallic restorations. Furthermore, the accuracy of the algorithm reached about 95%. This algorithm was constructed as a first step toward the development of an automated system that generates dental charts from images acquired by an intraoral scanner. The availability of such a system would greatly increase the efficiency of personal identification in the event of a major disaster.

scholarly journals CONTACT POINTS DISTRIBUTION ACCORDING TO THE ARTICULATOR TYPE: SEMIADJUSTABLE VERSUS NON ADAPTABLE VERSUS DIGITAL

2021 ◽  
Vol 67 (2) ◽  
pp. 112-119
Author(s):  
Oana Almăşan ◽  
◽  
Smaranda Buduru ◽  
Simona Iacob ◽  
Andreea Chisnoiu ◽  
...  
Keyword(s):  
Oral Cavity ◽  
Treatment Options ◽  
Clinical Situation ◽  
Limited Capacity ◽  
Intraoral Scanner ◽  
The Real ◽  
Occlusal Contact ◽  
Contact Points ◽  
Intraoral Scanning ◽  
Periodontal Ligaments

Objectives. To analyze the location and intensity of occlusal contact points using three types of articulators: non adjustable, semiadjustable and digital with aiming at improving the diagnostic and treatment options in dental medicine. Material and method. For analyzing the distribution of contact points, the casts of a patient were mounted in the non adjustable and semiadjustable articulator. Intraoral scanning was performed using an intraoral scanner (Trios 3Shape) and reviewed in a virtual articulator. Occlusion obtained by the three methods was compared to the clinical situation. Results. Contact points in maximum intercuspation, propulsion and lateral movements were analyzed. The points obtained by using the non adjustable articulator have been less intense and more unprecise. By digitizing the contact points, the image becomes more accurate and sharp. Discussion. The semiadjustable articulator reproduces the contact points which are consistent with the clinical situation. Major differences occur when using the non adjustable articulator, which has a limited capacity of reproducing the clinical movements, therefore the marks are non consistent with the real clinical situation. The digital articulator seems promising in terms of eccentric movements. Conclusions. However performing an articulator may be, the clinical maximum intercuspation will never be fittingly reproduced, due to the fact that articulators are rigid systems, whereas the oral cavity has an elasticity, resulting from the mandible, teeth and periodontal ligaments. Virtual articulators need to be further developed for more accurate results.

scholarly journals Intraoral Scanners in Personal Identification of Corpses: Usefulness and Reliability of 3D Technologies in Modern Forensic Dentistry

2020 ◽  
Vol 14 (1) ◽  
pp. 255-266
Author(s):  
Alessandra Putrino ◽  
Valerio Bruti ◽  
Marinelli Enrico ◽  
Ciallella Costantino ◽  
Barbato Ersilia ◽  
...  
Keyword(s):  
Comparative Method ◽  
Personal Identification ◽  
Forensic Sciences ◽  
Forensic Dentistry ◽  
Digital Models ◽  
Intraoral Scanning ◽  
Palatal Rugae ◽  
Clinic Practice ◽  
Set Up ◽  
The Stability

Aims: This study aims to verify the applicability of modern dental technologies and their related principles of use to the forensic sciences in the field of personal identification. Background: Personal identification has always had a major role in many legal and administrative actions regarding both living and death beings. The techniques used are much less advanced than the technologies potentially available. Objective: Modern technologies, available to the daily dental clinic practice, as intraoral scanners, combined in particular to the specialist skill in orthodontics, can help redefine the methods of personal identification according to the levels of accuracy, trueness and feasibility greater than those applied in traditional forensic dentistry. Methods: 23 corpses (12F;11M) have been selected for intraoral scanning with the Carestream 3500® digital device. The superimposition of initial and late digital models, digital models and radiographs (orthopantomography and full mouth periapical films) has been evaluated to verify the stability of some structures as palatal rugae after death and to assess intraoral scanning as a successful comparative method between antemortem and post-mortem records (digital models or radiographs). Obtained results were subjected to statistical analysis by the t-student test and X-square test with Yates correction (p<0.05). Results: After death, palatal rugae significatively change especially in mouths with restorations/prosthesis/missing teeth. The percentages of correct matching between scans and radiographs are very higher (up 90%; p<0.05). Conclusion: This study has been set up to study and develop new, reliable and fast methods of personal identification that can surpass many of the issues seen with the other techniques by a modern rugoscopy, a modern radiographic-digital comparison and virtual oral autopsy.

scholarly journals Intraoral Scanners in Personal Identification of Corpses: Usefulness and Reliability of 3D Technologies in Modern Forensic Dentistry

2020 ◽  
Vol 14 (1) ◽  
pp. 305-316
Author(s):  
Alessandra Putrino ◽  
Valerio Bruti ◽  
Marinelli Enrico ◽  
Ciallella Costantino ◽  
Barbato Ersilia ◽  
...  
Keyword(s):  
Comparative Method ◽  
Personal Identification ◽  
Forensic Sciences ◽  
Forensic Dentistry ◽  
Digital Models ◽  
Intraoral Scanning ◽  
Palatal Rugae ◽  
Clinic Practice ◽  
Set Up ◽  
The Stability

Aims: This study aims to verify the applicability of modern dental technologies and their related principles of use to the forensic sciences in the field of personal identification. Background: Personal identification has always had a major role in many legal and administrative actions regarding both living and death beings. The techniques used are much less advanced than the technologies potentially available. Objective: Modern technologies, available to the daily dental clinic practice, as intraoral scanners, combined in particular to the specialist skill in orthodontics, can help redefine the methods of personal identification according to the levels of accuracy, trueness and feasibility greater than those applied in traditional forensic dentistry. Methods: 23 corpses (12F;11M) have been selected for intraoral scanning with the Carestream 3500® digital device. The superimposition of initial and late digital models, digital models and radiographs (orthopantomography and full mouth periapical films) has been evaluated to verify the stability of some structures as palatal rugae after death and to assess intraoral scanning as a successful comparative method between antemortem and post-mortem records (digital models or radiographs). Obtained results were subjected to statistical analysis by the t-student test and X-square test with Yates correction (p<0.05). Results: After death, palatal rugae significatively change especially in mouths with restorations/prosthesis/missing teeth. The percentages of correct matching between scans and radiographs are very higher (up 90%; p<0.05). Conclusion: This study has been set up to study and develop new, reliable and fast methods of personal identification that can surpass many of the issues seen with the other techniques by a modern rugoscopy, a modern radiographic-digital comparison and virtual oral autopsy.

ICD10Net: An Artificial Intelligence Algorithm with Medical Background Conducts ICD-10-CM Coding Task with Outstanding Performance (Preprint)

2019 ◽  
Author(s):  
Chin Lin ◽  
Yu-Sheng Lou ◽  
Chia-Cheng Lee ◽  
Chia-Jung Hsu ◽  
Ding-Chung Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
General Hospital ◽  
Pearson Correlation ◽  
Model Performance ◽  
International Classification Of Diseases ◽  
Free Text ◽  
Daily Work ◽  
Medical Background ◽  
Icd 10 ◽  
F Measure

BACKGROUND An artificial intelligence-based algorithm has shown a powerful ability for coding the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) in discharge notes. However, its performance still requires improvement compared with human experts. The major disadvantage of the previous algorithm is its lack of understanding medical terminologies. OBJECTIVE We propose some methods based on human-learning process and conduct a series of experiments to validate their improvements. METHODS We compared two data sources for training the word-embedding model: English Wikipedia and PubMed journal abstracts. Moreover, the fixed, changeable, and double-channel embedding tables were used to test their performance. Some additional tricks were also applied to improve accuracy. We used these methods to identify the three-chapter-level ICD-10-CM diagnosis codes in a set of discharge notes. Subsequently, 94,483-labeled discharge notes from June 1, 2015 to June 30, 2017 were used from the Tri-Service General Hospital in Taipei, Taiwan. To evaluate performance, 24,762 discharge notes from July 1, 2017 to December 31, 2017, from the same hospital were used. Moreover, 74,324 additional discharge notes collected from other seven hospitals were also tested. The F-measure is the major global measure of effectiveness. RESULTS In understanding medical terminologies, the PubMed-embedding model (Pearson correlation = 0.60/0.57) shows a better performance compared with the Wikipedia-embedding model (Pearson correlation = 0.35/0.31). In the accuracy of ICD-10-CM coding, the changeable model both used the PubMed- and Wikipedia-embedding model has the highest testing mean F-measure (0.7311 and 0.6639 in Tri-Service General Hospital and other seven hospitals, respectively). Moreover, a proposed method called a hybrid sampling method, an augmentation trick to avoid algorithms identifying negative terms, was found to additionally improve the model performance. CONCLUSIONS The proposed model architecture and training method is named as ICD10Net, which is the first expert level model practically applied to daily work. This model can also be applied in unstructured information extraction from free-text medical writing. We have developed a web app to demonstrate our work (https://linchin.ndmctsgh.edu.tw/app/ICD10/).

scholarly journals Extensible artificial intelligence model predicts post-ablation AF recurrence using coronary sinus electrogram

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Huang ◽  
Z Zhang ◽  
K Lin ◽  
Z Zuo ◽  
Q Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Catheter Ablation ◽  
Coronary Sinus ◽  
Public Health Problem ◽  
Adverse Outcomes ◽  
Funding Source ◽  
Major Public Health Problem ◽  
Deep Convolutional Neural Networks ◽  
F Measure ◽  
Af Recurrence

Abstract Background Atrial fibrillation (AF) is a major public health problem with significant adverse outcomes and catheter ablation is a widely adopted treatment. The CABANA trial showed that catheter ablation reduced AF recurrence to a greater extent than medications. However, some of patients who underwent this procedure still experience relapse. Here, we present an innovative way to identify this subgroup using an artificial intelligence (AI) -assisted coronary sinus electrogram. Hypothesis Our hypothesis is that credible features in the electrogram can be extracted by AI for prediction, therefore rigorous drug administration, close follow-up or potential second procedure can be applied to these patients. Methods 67 patients from two independent hospitals (SPH & ZSH) with non-valvular persistent AF undergoing circumferential pulmonary vein isolation were enrolled in this study, 23 of which experienced recurrence 6 months after the procedure. We collected standard 2.5-second fragments of coronary sinus electrogram from ENSITE NAVX (SPH) and Carto (ZSH)system before the ablation started. A total of 1429 fragments were obtained and a transfer learning-based ResNet model was employed in our study. Fragments from ZSH were used for training and SPH for validation of deep convolutional neural networks (DCNN). The AI model performance was evaluated by accuracy, recall, precision, F-Measure and AUC. Results The prediction accuracy of the DCNN in single center reached 96%, while that in different ablation systems reached 74.3%. Also, the algorithm yielded values for the AUC, recall, precision and F-Measure of 0.76, 86.1%, 95.9% and 0.78, respectively, which shows satisfactory classification results and extensibility in different cardiology centers and brands of electroanatomic mapping instruments. Conclusions Our work has revealed the potential intrinsic correlation between coronary sinus electrical activity and AF recurrence using DCNN-based model. Moreover, the DCNN model we developed shows great prospects in the relapse prediction for personalized post-procedural management. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): The National Natural Science Foundation of China

scholarly journals Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data

2021 ◽  
Vol 14 ◽  
pp. 263177452199062
Author(s):  
Benjamin Gutierrez Becker ◽  
Filippo Arcadu ◽  
Andreas Thalhammer ◽  
Citlalli Gamez Serna ◽  
Owen Feehan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ulcerative Colitis ◽  
Clinical Trials ◽  
Deep Learning ◽  
Mayo Clinic ◽  
Automated System ◽  
Phase Iii ◽  
Plain Language ◽  
Data Set ◽  
Endoscopic Videos

Introduction: The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading. Methods: Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning–based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis. Results and Conclusion: Our evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation. Plain language summary Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

Digital Impression taking for Maxillary Full-Arch Restoration with Immediate Loading. A Case Report.

2021 ◽  
Author(s):  
Jesús Peláez Rico ◽  
Jorge Cortés-Bretón Brinkmann ◽  
María Carrión Martín ◽  
Mabel Albanchez González ◽  
Celia Tobar Arribas ◽  
...  
Keyword(s):  
Acrylic Resin ◽  
Digital Impression ◽  
Immediate Loading ◽  
Clinical Report ◽  
Intraoral Scanner ◽  
Intraoral Scanning ◽  
Digital Impressions ◽  
Removable Prosthesis ◽  
And Function

The aim of this clinical report is to describe a maxillary full-arch implant supported restoration with immediate loading performed by means of an entirely digital workflow with photogrammetric system and intraoral scanning. A female patient with an edentulous maxillary arch attended the dental clinic seeking a maxillary fixed restoration. After treatment planning, six implants were placed using a surgical splint fabricated digitally by intraoral scanning of her previous removable prosthesis. Multi-unit abutments were fitted and two digital impressions were taken, one with a photogrammetric system for determining implant positions, and the other with an intraoral scanner for soft tissue registration. The acrylic resin structure of the immediate prosthesis was milled and placed within 8 hours of implant surgery. This provisional structure fitted correctly and provided adequate esthetics and function. Radiographic and clinical follow-up after 24 months observed adequate implant evolution.

Optimization of Grease Dispensing Unit Using Fuzzy Logic Algorithm

2016 ◽  
Vol 852 ◽  
pp. 859-866
Author(s):  
Milind Havanur ◽  
A. Arockia Selvakumar
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Process Parameters ◽  
Environmental Conditions ◽  
Fuzzy Logic System ◽  
Automated System ◽  
Factors Affecting ◽  
Fuzzy Logic Algorithm ◽  
Optimal Quantity ◽  
Logic System

Grease dispensing unit is a well invented tool for greasing application which preserves health of operator working and ensures optimal quantity. There are fluctuations in the process of grease dispensing which is dependent on process parameters which make the grease dispensing. The properties of grease vary which depend on environmental conditions. In this paper the modeling of grease dispensing process using artificial intelligence method, fuzzy logic to optimize the flow of grease by considering the factors affecting the flow of grease and usage of automated system for grease dispensing process. The work involves usage of LabVIEW for modeling of fuzzy logic network Based on the results obtained a detailed discussions were made on how to implement the fuzzy logic system for optimization of flow of grease for the existing process. Further, the work also details the future scope of work that can be carried out.

scholarly journals Personal identification with artificial intelligence under COVID-19 crisis: a scoping review

2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Shinpei Matsuda ◽  
Hitoshi Yoshimura
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Literature Search ◽  
Personal Identification ◽  
Flow Diagram ◽  
Cochrane Library ◽  
Identification Methods ◽  
Institutional Infrastructure ◽  
The Face ◽  
Critical Consideration

Abstract Background Artificial intelligence is useful for building objective and rapid personal identification systems. It is important to research and develop personal identification methods as social and institutional infrastructure. A critical consideration during the coronavirus disease 2019 pandemic is that there is no contact between the subjects and personal identification systems. The aim of this study was to organize the recent 5-year development of contactless personal identification methods that use artificial intelligence. Methods This study used a scoping review approach to map the progression of contactless personal identification systems using artificial intelligence over the past 5 years. An electronic systematic literature search was conducted using the PubMed, Web of Science, Cochrane Library, CINAHL, and IEEE Xplore databases. Studies published between January 2016 and December 2020 were included in the study. Results By performing an electronic literature search, 83 articles were extracted. Based on the PRISMA flow diagram, 8 eligible articles were included in this study. These eligible articles were divided based on the analysis targets as follows: (1) face and/or body, (2) eye, and (3) forearm and/or hand. Artificial intelligence, including convolutional neural networks, contributed to the progress of research on contactless personal identification methods. Conclusions This study clarified that contactless personal identification methods using artificial intelligence have progressed and that they have used information obtained from the face and/or body, eyes, and forearm and/or hand.

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