A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System.

This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.

A robust deep learning model for the classification of dental implant brands.

OBJECTIVE: In cases where the brands of implants are not known, treatment options can be significantly limited in potential complications arising from implant procedures. This research aims to explore the application of deep learning techniques for the classification of dental implant systems using panoramic radiographs. The primary objective is to assess the superiority of the proposed model in achieving accurate and efficient dental implant classification. MATERIAL AND METHODS: A comprehensive analysis was conducted using a diverse set of 25 convolutional neural network (CNN) models, including popular architectures such as VGG16, ResNet-50, EfficientNet, and ConvNeXt. The dataset of 1258 panoramic radiographs from patients who underwent implant treatment at faculty of dentistry was utilized for training and evaluation. Six different dental implant systems were employed as prototypes for the classification task. The precision, recall, F1 score, and support scores for each class have included in the classification accuracy report to ensure accurate and reliable results from the model. RESULTS: The experimental results demonstrate that the proposed model consistently outperformed the other evaluated CNN architectures in terms of accuracy, precision, recall, and F1-score. With an impressive accuracy of 95.74 % and high precision and recall rates, the ConvNeXt model showcased its superiority in accurately classifying dental implant systems. Notably, the model's performance was achieved with a relatively smaller number of parameters, indicating its efficiency and speed during inference. CONCLUSION: The findings highlight the effectiveness of deep learning techniques, particularly the proposed model, in accurately classifying dental implant systems from panoramic radiographs.

Optimization of asymmetric bioreduction conditions of 1-(thiophen-2-yl)ethanone by Weissella cibaria N9 using a desirability function-embedded face-centered optimization model.

Prochiral ketones can be effectively bio-reduced to chiral secondary alcohols by whole-cell biocatalysts, which are possible useful precursors to synthesize physiologically active chemicals and natural products. When whole-cell biocatalysts strains are used, bioreduction process can be influenced by various cultural factors, and it is vital to optimize these factors that affect selectivity, conversion rate, and yield. In this study, Weissella cibaria N9 was used as whole-cell biocatalyst for bioreduction of 1-(thiophen-2-yl)ethanone, and cultural design factors were optimized using a desirability function-embedded face-centered optimization model. For this, effects of pH (4.5-5.5-6.5, x1), (2) temperature (25-30-35 °C, x2), (3) incubation period (24-48-72 h, x3), and (4) agitation speed (100-150-200 rpm, x4) on two response variables; (1) ee (%) and (2) cr (%) were tested. Next, desirability function-embedded face-centered optimization model revealed that a pH of 6.43, a temperature of 26.04 °C, an incubation period of 52.41 h, and an agitation speed of 150 rpm were the optimum levels and the estimated ee and cr responses were 99.31% and 98.16%, respectively. Importantly, the actual experimental ee and cr responses were similar to the estimated values indicating the capability of the offered desirability function-embedded face-centered optimization model when using the optimum cultural conditions.

An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets.

Colorectal cancer (CRC) is one of the common types of cancer with a high mortality rate. Colonoscopy is the gold standard for CRC screening and significantly reduces CRC mortality. However, due to many factors, the rate of missed polyps, which are the precursors of colorectal cancer, is high in practice. Therefore, many artificial intelligence-based computer-aided diagnostic systems have been presented to increase the detection rate of missed polyps. In this article, we present deep learning-based methods for reliable computer-assisted polyp detection. The proposed methods differ from state-of-the-art methods as follows. First, we improved the performances of YOLOv3 and YOLOv4 object detection algorithms by integrating Cross Stage Partial Network (CSPNet) for real-time and high-performance automatic polyp detection. Then, we utilized advanced data augmentation techniques and transfer learning to improve the performance of polyp detection. Next, for further improving the performance of polyp detection using negative samples, we substituted the Sigmoid-weighted Linear Unit (SiLU) activation functions instead of the Leaky ReLU and Mish activation functions, and Complete Intersection over Union (CIoU) as the loss function. In addition, we present a comparative analysis of these activation functions for polyp detection. We applied the proposed methods on the recently published novel datasets, which are the SUN polyp database and the PICCOLO database. Additionally, we investigated the proposed models for MICCAI Sub-Challenge on Automatic Polyp Detection in Colonoscopy dataset. The proposed methods outperformed the other studies in both real-time performance and polyp detection accuracy.

A robust real-time deep learning based automatic polyp detection system.

Colorectal cancer (CRC) is globally the third most common type of cancer. Colonoscopy is considered the gold standard in colorectal cancer screening and allows for the removal of polyps before they become cancerous. Computer-aided detection systems (CADs) have been developed to detect polyps. Unfortunately, these systems have limited sensitivity and specificity. In contrast, deep learning architectures provide better detection by extracting the different properties of polyps. However, the desired success has not yet been achieved in real-time polyp detection. Here, we propose a new structure for real-time polyp detection by scaling the YOLOv4 algorithm to overcome these obstacles. For this, we first replace the whole structure with Cross Stage Partial Networks (CSPNet), then substitute the Mish activation function for the Leaky ReLu activation function and also substituted the Distance Intersection over Union (DIoU) loss for the Complete Intersection over Union (CIoU) loss. We improved performance of YOLOv3 and YOLOv4 architectures using different structures such as ResNet, VGG, DarkNet53, and Transformers. To increase success of the proposed method, we utilized a variety of data augmentation approaches for preprocessing, an ensemble learning model, and NVIDIA TensorRT for post processing. In order to compare our study with other studies more objectively, we only employed public data sets and followed MICCAI Sub-Challenge on Automatic Polyp Detection in Colonoscopy. The proposed method differs from other methods with its real-time performance and state-of-the-art detection accuracy. The proposed method (without ensemble learning) achieved higher results than those found in the literature, precision: 91.62%, recall: 82.55%, F1-score: 86.85% on public ETIS-LARIB data set and precision: 96.04%, recall: 96.68%, F1-score: 96.36% on public CVC-ColonDB data set, respectively.

A comprehensive review of deep learning in colon cancer.

Deep learning has emerged as a leading machine learning tool in object detection and has attracted attention with its achievements in progressing medical image analysis. Convolutional Neural Networks (CNNs) are the most preferred method of deep learning algorithms for this purpose and they have an essential role in the detection and potential early diagnosis of colon cancer. In this article, we hope to bring a perspective to progress in this area by reviewing deep learning practices for colon cancer analysis. This study first presents an overview of popular deep learning architectures used in colon cancer analysis. After that, all studies related to colon cancer analysis are collected under the field of colon cancer and deep learning, then they are divided into five categories that are detection, classification, segmentation, survival prediction, and inflammatory bowel diseases. Then, the studies collected under each category are summarized in detail and listed. We conclude our work with a summary of recent deep learning practices for colon cancer analysis, a critical discussion of the challenges faced, and suggestions for future research. This study differs from other studies by including 135 recent academic papers, separating colon cancer into five different classes, and providing a comprehensive structure. We hope that this study is beneficial to researchers interested in using deep learning techniques for the diagnosis of colon cancer.

Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm.

As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks.

Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip.

Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks.