Machine Learning Applications in Dentistry

Artificial Intelligence has emerged as a breakthrough in many fields including medicine and dentistry where new approaches can be employed to solve challenging decision making processes faced in the dental field. Artificial intelligence can be used as a decision support mechanism to solve the increasing population and consequently the increasing dental treatment needs. It also assists dentists in diagnosis and treatment planning stages that require expert opinion. This mini-review covers some of the recent studies in this area and envisions future directions on the use of machine learning in dental problems

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.publishedVersionPeer reviewe

Full-endoscopic removal of third ventricular colloid cysts: technique, results, and limitations

IntroductionColloid cysts (CCs) are rare benign lesions that usually arise from the roof of the third ventricle. They may present with obstructive hydrocephalus and cause sudden death. Treatment options include ventriculoperitoneal shunting, cyst aspiration, and cyst resection microscopically or endoscopically. This study aims to report and discuss the full-endoscopic technique for removing colloid cysts.Materials and methodsA 25°-angled neuroendoscope with an internal working channel diameter of 3.1 mm and a length of 122 mm is used. The authors described the technique of resecting a colloid cyst by a full-endoscopic procedure and evaluated the surgical, clinical, and radiological results.ResultsTwenty-one consecutive patients underwent an operation with a transfrontal full-endoscopic approach. The swiveling technique (grasping the cyst wall and rotational movements) was used for CC resection. Of these patients, 11 were female, and ten were male (mean age, 41 years). The most frequent initial symptom was a headache. The mean cyst diameter was 13.9 mm. Thirteen patients had hydrocephalus at admission, and one needed shunting after cyst resection. Seventeen patients (81%) underwent total resection; 3 (14%), subtotal resection; and 1 (5%), partial resection. There was no mortality; one patient had permanent hemiplegia, and one had meningitis. The mean follow-up period was 14 months.ConclusionEven though microscopic resection of cysts has been widely used as a gold standard, successful endoscopic removal has been described recently with lower complication rates. Applying angled endoscopy with different techniques is essential for total resection. Our study is the first case series to show the outcomes of the swiveling technique with low recurrence and complication rates

Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome

Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data. The dataset was obtained from NCBI preprocessed graph representations and converted into a structured form. Seven well-known Machine Learning frameworks, including Random Forest, Support Vector Machines, Extreme Gradient Boosting, Light Gradient Boosting Machine, Gaussian Naïve Bayes, Logistic Regression, and k-Nearest Neighbor, were used. Grid Search was employed for hyperparameter optimization. The performance of the Machine Learning models was evaluated using various metrics such as accuracy, precision, fscore, kappa, and area under the receiver operating characteristic curve. Additionally, Mc Nemar’s test was conducted to assess the statistical significance of the experiment. The data was constructed using k-mer lengths of 3, 4 and 5. The Light Gradient Boosting Machine model overperformed over other models with 67.24%, 74.63% and 76.47% accuracy for k-mer lengths of 3, 4 and 5, respectively. The LightGBM model also demonstrated the best performance in each metric. The study showed promising results predicting disease from raw sequence data. Finally, Mc Nemar’s test results found statistically significant differences between different Machine Learning approaches

Implications of circulating irisin and Fabp4 levels in patients with polycystic ovary syndrome

The aim of the study was to evaluate the fatty acid-binding protein-4 (FABP4) and irisin concentrations in women with polycystic ovary syndrome (PCOS). Forty-nine women with PCOS, diagnosed according to Rotterdam criteria and 39 healthy women matched for body mass index (BMI) and age. Serum irisin and plasma FABP4 concentrations were measured in both groups. The association of irisin and FABP4 concentrations with metabolic parameters were also tested. Women with PCOS had significantly lower mean serum irisin concentrations than control subjects (158.5 +/- 123.3 versus 222.9 +/- 152.2ng/ml, p0.05). FABP4 concentrations were correlated with BMI, waist-hip ratio (WHR) and HOMA-IR (r=0.57, p=0.001; r=0.26, p=0.03; r=0.26, p=0.03, respectively). No associations between irisin and all the others parameters except serum levels of LH were found. Serum irisin concentrations of women with PCOS were lower compared to the controls. Moreover, there were no difference in plasma FABP4 concentrations between women with PCOS and controls

An Examination of Turkish Early Childhood Teachers' Challenges in Implementing Pedagogical Documentation

This study examined the challenges of pedagogical documentation from the perspectives of Turkish early childhood teachers. Pedagogical documentation was initially introduced as a teaching, learning, and assessment tool in early childhood education settings in Turkey through a three-year project. A total of 22 early childhood teachers working in a variety of early childhood programs participated in the study. Data were gathered via semi-structured and focus group interviews at the end of an intervention on the use of pedagogical documentation. The data analysis was based on an inductive approach, revealing that teachers encountered functional and attitudinal challenges in implementing pedagogical documentation. Three major themes or challenges emerged in this study: (a) challenges originating from contextual elements, (b) challenges originating from the nature of pedagogical documentation, and (c) challenges related to the adaptations of teachers to the pedagogical documentation process. The findings underline the need to transform teachers' perspectives so that they are more amenable to child-centered practices while acknowledging the importance of collaboration with all stakeholders. In-service and pre-service training should be planned to support teachers' understanding of pedagogical documentation and to ensure that teachers employ the pedagogical documentation process in their own practices

A single-center experience of transsphenoidal endoscopic surgery for acromegaly in 73 patients: results and predictive factors for remission

Background Transsphenoidal endoscopic surgery is the first-line treatment for growth hormone-secreting adenomas. Objective To analyse the results of the transsphenoidal endoscopic approach for acromegaly and to determine the predictive factors of remission. Methods A single-centre retrospective review was performed in patients who underwent endoscopic transsphenoidal surgery for acromegaly between January 2009 and January 2019. Demographic features, clinical presentation, histopathology records, complications and pre- and postoperative radiologic and endocrinological assessments were evaluated. The factors that influenced the remission rates were investigated. Results A total of 73 patients underwent surgery via the transsphenoidal endoscopic approach. Cavernous sinus invasion was detected in 32 patients (43.8%); and macroadenoma, in 57 (78%). The pathology specimens of the 27 patients (36.9%) showed dual-staining adenomas with prolactin. A total of 51 patients (69.8%) attained biochemical remission 1 year after surgery. A second operation was performed in 10 patients (13.6%) with residual tumours without biochemical remission in the first year. Six (60%) of the patients attained remission at the last follow-up. Transient diabetes insipidus was observed in 18 patients (24.6%); and rhinorrhoea, which was resolved with conservative treatment, in 4 (5.4%). None of the patients developed panhypopituitarism. The presence of cavernous sinus invasion and preoperative IGF-1, immediate postoperative GH and third-month IGF-1 levels were predictive of remission. Conclusion Transsphenoidal endoscopic surgery is a safe and effective treatment for acromegaly. Reoperation should be considered in patients with residual tumours without remission

Bacterial Agents Causing Meningitis During 2013-2014 in Turkey: A Multi-Center Hospital-Based Prospective Surveillance Study

This is an observational epidemiological study to describe causes of bacterial meningitis among persons between 1 month and 18 y of age who are hospitalized with suspected bacterial meningitis in 7 Turkish regions. covering 32% of the entire population of Turkey. We present here the results from 2013 and 2014. A clinical case with meningitis was defined according to followings: any sign of meningitis including fever, vomiting, headache, and meningeal irritation in children above one year of age and fever without any documented source, impaired consciousness, prostration and seizures in those < 1 y of age. Single tube multiplex PCR assay was performed for the simultaneous identification of bacterial agents. The specific gene targets were ctrA, bex, and ply for N. meningitidis, Hib, and S. pneumoniae, respectively. PCR positive samples were recorded as laboratory-confirmed acute bacterial meningitis. A total of 665 children were hospitalized for suspected acute meningitis. The annual incidences of acute laboratory-confirmed bacterial meningitis were 0.3 cases / 100,000 population in 2013 and 0.9 cases/100,000 in 2014. Of the 94 diagnosed cases of bacterial meningitis by PCR, 85 (90.4%) were meningococcal and 9 (9.6%) were pneumococcal. Hib was not detected in any of the patients. Among meningococcal meningitis, cases of serogroup Y, A, B and W-135 were 2.4% (n = 2), 3.5% (n = 3), 32.9% (n = 28), and 42.4% (n = 36). No serogroup C was detected among meningococcal cases. Successful vaccination policies for protection from bacterial meningitis are dependent on accurate determination of the etiology of bacterial meningitis. Additionally, the epidemiology of meningococcal disease is dynamic and close monitoring of serogroup distribution is comprehensively needed to assess the benefit of adding meningococcal vaccines to the routine immunization program.Wo