An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism

Aim: The present study is aimed to assess the segmentation success of an artificial intelligence (AI) system based on the deep convolutional neural network (D-CNN) method for the segmentation of masseter muscles on ultrasonography (USG) images. Materials and Methods: This retrospective study was carried out by using the radiology archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry in Ankara University. A total of 195 anonymized USG images were used in this retrospective study. The deep learning process was performed using U-net, Pyramid Scene Parsing Network (PSPNet), and Fuzzy Petri Net (FPN) architectures. Muscle thickness was assessed using USG by manual segmentation and measurements using USG’s software. The neural network model (CranioCatch, Eskisehir-Turkey) was then used to determine the muscles, following automatic measurements of the muscles. Accuracy, ROC area under the curve (AUC), and Precision-Recall Curves (PRC) AUC were calculated in the test dataset and compare a human observer and the AI model. Manual segmentation and measurements were compared statistically with AI ( P < .05). The Mann–Whitney U test was used to analyze whether there is a statistically significant difference between the predicted values and the actual values. Results: The AI models detected and segmented all test muscle data for FPN and U-net, while only two cases of muscles were not detected by PSPNet (false negatives). Accuracies of FPN, PSPNet, and U-net were estimated as 0.985, 0.947, and 0.969, respectively. Receiver operating characteristic scores of FPN, PSPNet, and U-net were estimated as 0.977, 0.934, and 0.969, respectively. The D-CNN measurements of the muscles were similar to manual measurements. There was no significant difference between the two measurement methods in three groups ( P > .05). Conclusion: The proposed AI system approach for the analysis of USG images seems to be promising for automatic masseter muscle segmentation and measurement of thickness. This method can help surgeons, radiologists, and other professionals such as physical therapists in evaluating the time correctly and saving time for diagnosis.

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Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽

10.1136/bmjopen-2020-046265 ◽

2021 ◽

Vol 11 (6) ◽

pp. e046265

Author(s):

Shotaro Doki ◽

Shinichiro Sasahara ◽

Daisuke Hori ◽

Yuichi Oi ◽

Tsukasa Takahashi ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Psychological Distress ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional ◽

Explanatory Variables ◽

The Neural Network ◽

Significant Difference ◽

Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

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Statistical Methods and Artificial Neural Networks Techniques in Electromyography

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2012010103 ◽

2012 ◽

Vol 1 (1) ◽

pp. 39-47 ◽

Cited By ~ 3

Author(s):

Ahmad Taher Azar ◽

Valentina E. Balas

Keyword(s):

Neural Network ◽

Statistical Methods ◽

Muscle Activity ◽

Masseter Muscle ◽

Discrete Wavelet ◽

Neural Network Classifier ◽

Denture Base ◽

Feature Vectors ◽

Significant Difference ◽

Before And After

This work represents a comparative study for the activity of the masseter muscle for patients before trial base denture insertion and the activity of the same muscle after trial denture base insertion for both right and left masseter muscles. The study tried to find if there were significant differences in the activity of the masseter muscle before and after patients wearing their trial denture base using two approaches: parametric statistical methods and a Neural Network Classifier. Statistical analysis was performed on three feature vectors extracted from autoregressive (AR) modeling, Discrete Wavelet Transform (WT), and from Wavelet Packet Transform (WP). The least significant difference test and the student t-test have not proved significant differences in the masseter muscle activity before and after wearing denture. However, using the same feature vectors, a neural network classifier has proved that there are significant differences in the masseter muscle activity before and after patients wearing trial denture base.

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Comparison of Two Different Treatment Methods to Treat the Temporomandibular Joint Disorders (Myofascial Pain) in a Short Period of Time - A Retrospective Study

Journal of Evolution of Medical and Dental Sciences ◽

10.14260/jemds/2021/678 ◽

2021 ◽

Vol 10 (38) ◽

pp. 3342-3345

Author(s):

Hamad Nasser Albageah ◽

Abdulaziz Abdulhakim Alwakeel

Keyword(s):

Retrospective Study ◽

Temporomandibular Joint ◽

Masseter Muscle ◽

Orofacial Pain ◽

Myofascial Pain ◽

Temporomandibular Joint Disorder ◽

Short Period ◽

Significant Difference ◽

Joint Disorder ◽

Pain Patients

BACKGROUND Temporomandibular joint(TMJ)is the third most common site of pain in the orofacial area, while the masseter muscle was the primary painful masticatory muscle. The temporal and frontal region were pain areas indicated by temporomandibular joint disorder (TMDs) patients. The purpose of this study was to compare two different treatment modalities, physical therapy and occlusal appliance to treat myofascial pain. METHODS This retrospective study comprises of all orofacial pain patients attending orofacial pain clinics of Dental University Hospital, King Saud University in Riyadh, Saudi Arabia. Patients were categorized into two groups, the first group: patients treated by the occlusal appliance (hard type). The second group: patients treated by physiotherapy home exercises, including posture position modification. Physical therapy included muscle stretching and isometric tension against resistance exercises and guided jaw movements. Methods of clinical examination was based on the research diagnostic criteria for temporomandibular disorders (RDC/TMD) criteria. The data of pain level was collected based on the visual analog scale (VAS). RESULTS 16.1 % of patients were male, and 83.9 % of the patients were female. With the mean age being 31.1 years old. 92.9 % were Saudi patients and 7.1 % were non-Saudi. 50 % of the patients were using an occlusal appliance, and 50 % went for physiotherapy. The independent t-test showed a highly significant difference between different management methods with a P – value of 0.038 and a mean difference of 0.32143. 80.5 % of the patients reported masseter muscle pain as one of their main complaints. CONCLUSIONS A significant difference was observed between physiotherapy and occlusal appliances with education in treating patients with myofascial pain. Patients using the occlusal appliances showed a high percentage of pain reduction (85.7 %) compared to physiotherapy treatment (57.1 %) in a short period of time. Henceforth, patient’s education plays a significant role in pain reduction. KEY WORDS Temporomandibular Joint Disorder, Occlusal Appliance, Myofascial Pain, Physiotherapy

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Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

Nature Communications ◽

10.1038/s41467-021-26643-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Gang Yu ◽

Kai Sun ◽

Chao Xu ◽

Xing-Hua Shi ◽

Chong Wu ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Deep Learning ◽

Supervised Learning ◽

Area Under The Curve ◽

Patient Level ◽

Significant Difference ◽

The Mean ◽

Whole Slide Images ◽

Better Than

AbstractMachine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subjects from 13 independent centers. SSL (~3150 labeled, ~40,950 unlabeled; ~6300 labeled, ~37,800 unlabeled patches) performs significantly better than the SL. No significant difference is found between SSL (~6300 labeled, ~37,800 unlabeled) and SL (~44,100 labeled) at patch-level diagnoses (area under the curve (AUC): 0.980 ± 0.014 vs. 0.987 ± 0.008, P value = 0.134) and patient-level diagnoses (AUC: 0.974 ± 0.013 vs. 0.980 ± 0.010, P value = 0.117), which is close to human pathologists (average AUC: 0.969). The evaluation on 15,000 lung and 294,912 lymph node images also confirm SSL can achieve similar performance as that of SL with massive annotations. SSL dramatically reduces the annotations, which has great potential to effectively build expert-level pathological artificial intelligence platforms in practice.

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Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies

Modern Pathology ◽

10.1038/s41379-021-00826-6 ◽

2021 ◽

Author(s):

Andrea Chatrian ◽

Richard T. Colling ◽

Lisa Browning ◽

Nasullah Khalid Alham ◽

Korsuk Sirinukunwattana ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Area Under The Curve ◽

Validation Dataset ◽

Prostate Biopsies ◽

The Neural Network ◽

Neural Network Prediction ◽

Neural Network Algorithm ◽

Network Prediction ◽

Whole Slide Images

AbstractThe use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide-level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by threefold cross-validation. Validation was conducted on a separate validation dataset of 222 images. Non IHC-requested cases were diagnosed in 17.9 min on average, while IHC-requested cases took 33.4 min over multiple reporting sessions. We estimated 11 min could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.

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Artificial Intelligence for Advance Requesting of Immunohistochemistry in Diagnostically Uncertain Prostate Biopsies

10.1101/2021.02.20.21252126 ◽

2021 ◽

Author(s):

Andrea Chatrian ◽

Richard Colling ◽

Lisa Browning ◽

Nasullah Khalid Alham ◽

Korsuk Sirinukunwattana ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Area Under The Curve ◽

Validation Dataset ◽

Prostate Biopsies ◽

The Neural Network ◽

Neural Network Prediction ◽

Neural Network Algorithm ◽

Network Prediction ◽

Whole Slide Images

The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by 3-fold cross-validation. Validation was conducted on a separate validation dataset of 212 images. Non IHC-requested cases were diagnosed in 17.9 minutes on average, while IHC-requested cases took 33.4 minutes over multiple reporting sessions. We estimated 11 minutes could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.

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Feasibility study of relative renal function assessment by contrast-enhanced abdominal CT in comparison to 99mTc-MAG3 renal scintigraphy

Insight Urology ◽

10.52786/isu.a.25 ◽

2021 ◽

Vol 42 (1) ◽

pp. 46-50

Author(s):

Jittapat Kalapong ◽

◽

Tanet Thaidumrong ◽

Seksan Chitwiset ◽

◽

...

Keyword(s):

Renal Function ◽

Retrospective Study ◽

Strong Correlation ◽

Renal Scintigraphy ◽

Manual Segmentation ◽

Post Processing ◽

Abdominal Ct ◽

Contrast Enhanced ◽

Significant Difference ◽

Function Assessment

Objective: To determine the feasibility of using contrast-enhanced abdominal CT to assess relative renal function. Materials and Methods: This retrospective study reviewed data from 32 patients who had had investigations by contrast-enhanced abdominal CT and 99mTc-MAG3 renal scintigraphy, within a period of not more than 30 days. Post-processing CT images of kidneys were by manual segmentation and calculated to interpret the relative renal function. Results: There was strong correlation between CT derived relative renal function and 99mTc-MAG3 renal scintigraphy (r = 0.971, p < 0.001) and no statistically significant difference in renal function between the two techniques (p = 0.572). Conclusion: Contrast-enhanced abdominal CT can determine relative renal function as accurately as renal scintigraphy. It is an appropriate alternative method, especially in hospitals where renal scintigraphy is not available.

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Masseter Muscle Thickness in Unilateral Partial Edentulism: An Ultrasonographic Study

Journal of Clinical Imaging Science ◽

10.4103/jcis.jcis_50_17 ◽

2017 ◽

Vol 7 ◽

pp. 44 ◽

Cited By ~ 3

Author(s):

S Sathasivasubramanian ◽

P M Venkatasai ◽

C V Divyambika ◽

Rupesh Mandava ◽

R Jeffrey ◽

...

Keyword(s):

Masseter Muscle ◽

Muscle Thickness ◽

Test Duration ◽

Present Sample ◽

Institutional Ethics ◽

Significant Difference ◽

The Difference ◽

Edentulous Patients ◽

And Function ◽

Experimental Group

Introduction: Teeth and facial muscles play a very important role in occlusal equilibrium and function. Occlusal derangement, seen in unilateral partially edentulous individuals, has an effect on masseter muscle anatomy and function. The present study aims to evaluate masseter muscle thickness in unilateral partial edentulism. Patients and Methods: Institutional ethics committee approval was obtained before the commencement of the study. The study involved patients who routinely visited the Department of Oral Medicine and Radiology, Sri Ramachandra University. The study sample included 27 unilateral edentulous patients (Group E) and 30 controls (Group C). The masseter muscle thickness was evaluated using high-resolution ultrasound real-time scanner (linear transducer − 7.5–10 MHz) at both relaxed and contracted states. Statistical Analysis Used: The results were analyzed using paired t-test and independent t-test. Duration of edentulism and muscle thickness was assessed using Pearson's correlation coefficient. Results: The study patients’ age ranged between 25 and 48 years (mean – 36 years). The comparative evaluation of masseter muscle thickness between the dentulous and edentulous sides of experimental group was statistically significant (P < 0.05). However, no statistically significant difference in masseter muscle thickness was found between the dentulous side of control and experimental groups. The correlation between the duration of partial edentulism and muscle thickness was statistically insignificant. Conclusion: The study proves masseter atrophy in the edentulous side. However, since the difference is found to be marginal with the present sample, a greater sample is necessary to establish and prove the present findings as well as to correlate with the duration of edentulism. Further studies are aimed to assess the muscle morphology after prosthetic rehabilitation.

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Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease

Scientific Reports ◽

10.1038/s41598-021-90411-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mohammadreza Zandehshahvar ◽

Marly van Assen ◽

Hossein Maleki ◽

Yashar Kiarashi ◽

Carlo N. De Cecco ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Area Under The Curve ◽

Valuable Insight ◽

X Rays ◽

Single Patient ◽

New Approach ◽

Clinical Model ◽

Average Area ◽

Learning Technique

AbstractWe report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of the disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with the average Area Under the Curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in a single patient and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in the early stages of the disease. This use of AI to assess the severity and possibly predicting the future stages of the disease early on, will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.

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The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

Frontiers in Psychology ◽

10.3389/fpsyg.2021.731713 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chengjin Xu ◽

Zhe Zhang

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

College Students ◽

Doctoral Students ◽

Self Efficacy ◽

Psychological Capital ◽

Law Students ◽

Four Dimensions ◽

Significant Difference ◽

The Difference

With the increasingly serious employment situation in China, the government and schools encourage college students to start businesses to alleviate employment pressure. College student's successful entrepreneurship depends on national preferential policies, social support, and, most importantly, their healthy and solid psychological quality and entrepreneurial psychological quality. The purpose is to understand the entrepreneurial psychology of college students and study the entrepreneurial psychological effect. Firstly, the four aspects of entrepreneurial psychology are summarized, including entrepreneurial awareness, entrepreneurial volition, entrepreneurial ability, and entrepreneurial personality. Secondly, the research status of college students' entrepreneurial psychology is reviewed, and the existing problems are pointed out. Thirdly, the combined model of wavelet transform and Neural Network (NN) is proposed, and the feasibility of the proposed model is evaluated through the analysis of college students' entrepreneurial psychology. The wavelet NN is used in experimental design to predict college students' entrepreneurial psychology, and the predicted results are compared with the actual value. From the perspective of the prediction results of entrepreneurial psychology, the combination of wavelet algorithm and neural network is more accurate for entrepreneurial psychology prediction and evaluation results of law students. Overall, the difference between the predicted value and the actual value is within 0.3 points, which is relatively stable. According to the analysis of single-factor results, the scores of students of different majors in the four dimensions of entrepreneurial psychology are all higher than 3.5, but there is no significant difference among the four dimensions (P > 0.05), indicating that the major has no significant impact on entrepreneurial psychology; law students with different educational backgrounds have significant differences in entrepreneurial psychology (P < 0.05), among which students with a master's degree have the strongest entrepreneurial will, while doctoral students have the lowest entrepreneurial will; in terms of entrepreneurial psychological capital, men's self-efficacy is higher than women's, and the difference is significant (P < 0.05). The difference between males and females in the scores of entrepreneurial psychological factors' four aspects is not very obvious. In terms of entrepreneurial psychological capital, males' self-efficacy is significantly higher than females' (P < 0.05). Artificial Intelligence (AI) technology has great application prospects in the prediction and evaluation of college students' entrepreneurial psychology, and college students' entrepreneurial psychology is highly correlated with gender and education.

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