Developing deep learning methods for classification of teeth in dental panoramic radiography.

OBJECTIVES: We aimed to develop an artificial intelligence-based clinical dental decision-support system using deep-learning methods to reduce diagnostic interpretation error and time and increase the effectiveness of dental treatment and classification. STUDY DESIGN: We compared the performance of 2 deep-learning methods, Faster Regions With the Convolutional Neural Networks (R-CNN) and You Only Look Once V4 (YOLO-V4), for tooth classification in dental panoramic radiography to determine which is more successful in terms of accuracy, time, and detection ability. Using a method based on deep-learning models trained on a semantic segmentation task, we analyzed 1200 panoramic radiographs selected retrospectively. In the classification process, our model identified 36 classes, including 32 teeth and 4 impacted teeth. RESULTS: The YOLO-V4 method achieved a mean 99.90% precision, 99.18% recall, and 99.54% F1 score. The Faster R-CNN method achieved a mean 93.67% precision, 90.79% recall, and 92.21% F1 score. Experimental evaluations showed that the YOLO-V4 method outperformed the Faster R-CNN method in terms of accuracy of predicted teeth in the tooth classification process, speed of tooth classification, and ability to detect impacted and erupted third molars. CONCLUSIONS: The YOLO-V4 method outperforms the Faster R-CNN method in terms of accuracy of tooth prediction, speed of detection, and ability to detect impacted third molars and erupted third molars. The proposed deep learning based methods can assist dentists in clinical decision making, save time, and reduce the negative effects of stress and fatigue in daily practice.

Convolutional neural network analysis of recurrence plots for high resolution melting classification.

BACKGROUND AND OBJECTIVE: High resolution melting (HRM) analysis is a rapid and correct method for identification of species, such as, microorganism, bacteria, yeast, virus, etc. HRM data are produced using real-time polymerase chain reaction (PCR) and unique for each species. Analysis of the HRM data is important for several applications, such as, for detection of diseases (e.g., influenza, zika virus, SARS-Cov-2 and Covid-19 diseases) in health, for identification of spoiled foods in food industry, for analysis of crime scene evidence in forensic investigation, etc. However, the characteristics of the HRM data can change due to the experimental conditions or instrumental settings. In addition, it becomes laborious and time-consuming process as the number of samples increases. Because of these reasons, the analysis and classification of the HRM data become challenging for species which have similar characteristics. METHODS: To improve the classification accuracy of HRM data, we propose to use image (visual) representation of HRM data, which we call HRM images, that are generated using recurrence plots, and propose convolutional neural network (CNN) based models for classifying HRM images. In this study, two different types of recurrence plots are generated, which are black-white recurrence plots (BW-RP) and gray scale recurrence plots (GS-RP) and four different CNN models are proposed for classifying HRM data. RESULTS: The classification performance of the proposed methods are evaluated based on average classification accuracy and F1 score, specificity, recall, and precision values for each yeast species. When BW-RP representation of HRM data is used as input to the CNN models, the best classification accuracy of 95.2% is obtained. The classification accuracies of CNN models for melting curve and GS-RP data representations of HRM data are 90.13% and 86.13%, respectively. The classification accuracy of support vector machines (SVM) model that take melting curve representation of HRM data is 86.53%. Moreover, when BW-RP representation of HRM data is used as input to the CNN models, the F1 score, specificity, recall and precision values are the highest for almost all of species. CONCLUSIONS: Experimental results show that using BW-RP representation of HRM data improved the classification accuracy of HRM data and CNN models that take these images as input outperformed CNN models that take melting curve and GS-RP representations of HRM data as inputs and SVM model that take melting curve representation of HRM data as input.

Multi fragment melting analysis system (MFMAS) for one-step identification of lactobacilli.

The accurate identification of lactobacilli is essential for the effective management of industrial practices associated with lactobacilli strains, such as the production of fermented foods or probiotic supplements. For this reason, in this study, we proposed the Multi Fragment Melting Analysis System (MFMAS)-lactobacilli based on high resolution melting (HRM) analysis of multiple DNA regions that have high interspecies heterogeneity for fast and reliable identification and characterization of lactobacilli. The MFMAS-lactobacilli is a new and customized version of the MFMAS, which was developed by our research group. MFMAS-lactobacilli is a combined system that consists of i) a ready-to-use plate, which is designed for multiple HRM analysis, and ii) a data analysis software, which is used to characterize lactobacilli species via incorporating machine learning techniques. Simultaneous HRM analysis of multiple DNA fragments yields a fingerprint for each tested strain and the identification is performed by comparing the fingerprints of unknown strains with those of known lactobacilli species registered in the MFMAS. In this study, a total of 254 isolates, which were recovered from fermented foods and probiotic supplements, were subjected to MFMAS analysis, and the results were confirmed by a combination of different molecular techniques. All of the analyzed isolates were exactly differentiated and accurately identified by applying the single-step procedure of MFMAS, and it was determined that all of the tested isolates belonged to 18 different lactobacilli species. The individual analysis of each target DNA region provided identification with an accuracy range from 59% to 90% for all tested isolates. However, when each target DNA region was analyzed simultaneously, perfect discrimination and 100% accurate identification were obtained even in closely related species. As a result, it was concluded that MFMAS-lactobacilli is a multi-purpose method that can be used to differentiate, classify, and identify lactobacilli species. Hence, our proposed system could be a potential alternative to overcome the inconsistencies and difficulties of the current methods.

Structural profile matrices for predicting structural properties of proteins.

Predicting structural properties of proteins plays a key role in predicting the 3D structure of proteins. In this study, new structural profile matrices (SPM) are developed for protein secondary structure, solvent accessibility and torsion angle class predictions, which could be used as input to 3D prediction algorithms. The structural templates employed in computing SPMs are detected by eight alignment methods in LOMETS server, gap affine alignment method, ScanProsite, PfamScan, and HHblits. The contribution of each template is weighted by its similarity to target, which is assessed by several sequence alignment scores. For comparison, the SPMs are also computed using Homolpro, which uses BLAST for target template alignments and does not assign weights to templates. Incorporating the SPMs into DSPRED classifier, the prediction accuracy improves significantly as demonstrated by cross-validation experiments on two difficult benchmarks. The most accurate predictions are obtained using the SPMs derived by threading methods in LOMETS server. On the other hand, the computational cost of computing these SPMs was the highest.

Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network.

Machine learning and deep learning methods aims to discover patterns out of datasets such as, microarray data and medical data. In recent years, the importance of producing microarray data from tissue and cell samples and analyzing these microarray data has increased. Machine learning and deep learning methods have been started to use in the diagnosis and classification of microarray data of cancer diseases. However, it is challenging to analyze microarray data due to the small number of sample size and high number of features of microarray data and in some cases some features may not be relevant with the classification. Because of this reason, studies in the literature focused on developing feature selection/dimension reduction techniques and classification algorithms to improve classification accuracy of the microarray data. This study proposes hybrid methods by using Relief and stacked autoencoder approaches for dimension reduction and support vector machines (SVM) and convolutional neural networks (CNN) for classification. In the study, three microarray datasets of Overian, Leukemia and Central Nervous System (CNS) were used. Ovarian dataset contains 253 samples, 15,154 genes and 2 classes, Leukemia dataset contains 72 samples, 7129 genes, and 2 classes and CNS dataset contains 60 samples, 7129 genes and 2 classes. Among the methods applied to the three microarray data, the best classification accuracy without dimension reduction was observed with SVM as 96.14% for ovarian dataset, 94.83% for leukemia dataset and 65% for CNS dataset. The proposed hybrid method ReliefF + CNN method outperformed other approaches. It gave 98.6%, 99.86% and 83.95% classification accuracy for the datasets of ovarian, leukemia, and CNS datasets, respectively. Results shows that dimension reduction methods improved the classification accuracy of the methods of SVM and CNN.

Developing structural profile matrices for protein secondary structure and solvent accessibility prediction.

MOTIVATION: Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target. RESULTS: In this paper, a new structural profiling technique is proposed that is based on deriving PFAM families and is combined with an existing approach. Cross-validation experiments on two benchmark datasets and at various similarity intervals demonstrate that the proposed profiling strategy performs significantly better than Homolpro, a state-of-the-art method for incorporating template information, as assessed by statistical hypothesis tests. AVAILABILITY AND IMPLEMENTATION: The DSPRED method can be accessed by visiting the PSP server at http://psp.agu.edu.tr. Source code and binaries are freely available at https://github.com/yusufzaferaydin/dspred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Assessment of Multi Fragment Melting Analysis System (MFMAS) for the Identification of Food-Borne Yeasts.

Multi Fragment Melting Analysis System (MFMAS) is a novel approach that was developed for the species-level identification of microorganisms. It is a software-assisted system that performs concurrent melting analysis of 8 different DNA fragments to obtain a fingerprint of each strain analyzed. The identification is performed according to the comparison of these fingerprints with the fingerprints of known yeast species recorded in a database to obtain the best possible match. In this study, applicability of the yeast version of the MFMAS (MFMAS-yeast) was evaluated for the identification of food-associated yeast species. For this purpose, in this study, a total of 145 yeast strains originated from foods and beverages and 19 standard yeast strains were tested. The DNAs isolated from these yeast strains were analyzed by the MFMAS, and their species were successfully identified with a similarity rate of 95% or higher. It was shown that the strains belonged to 43 different yeast species that are widely found in the foods. A clear discrimination was also observed in the phylogenetically related species. In conclusion, it might be suggested that the MFMAS-yeast seems to be a highly promising approach for a rapid, accurate, and one-step identification of the yeasts isolated from food products and/or their processing environments.