In dentistry, endodontics is the study of dental pulp and tissues surrounding the roots. Endodontic treatment is otherwise called root canal treatment. The importance of endodontics focuses on several therapies to protect human teeth from cavities or infections, injuries, and various oral diseases like oral cancer and periodontal disease. Over 3.5 billion people are affected by various oral diseases, 10% of the global population is affected by periodontal diseases, and 530 million children suffer from tooth decay. There are different types of root canal morphology and configurations in which multiple abnormalities exist, such as C-shaped canals, fusion of roots, dens invaginatus, distolingual root, taurodontism, root dilaceration, etc. AI plays a vital role in endodontic applications. Using AI for the 98prediction and diagnosis of periapical lesions, root fractures can be detected. Nowadays, AI is used to determine working length measurements, predict dental pulp stem cells, and guide retreatment procedures. Therefore, AI provides successful outcomes and improvements in diagnosis and prediction in root canal applications in day-to-day practices. This review chapter summarizes different deep learning techniques that can be implemented in various endodontic applications in detail to understand the pros and cons

Precise segmentation of retinal blood vessels (RBVs) is pivotal in ophthalmology research, aiding in detecting diverse retinal abnormalities. This study proposes a contrast-limited adaptive histogram equalisation (CLAHE) technique to improve retinal image quality and visibility of microvascular structures. We aimed to determine the complexity of blood vessels using fractal dimensions (FD) and compare different metrics for their effectiveness. We employed the UNet architecture to separate blood vessels, and our results on the DRIVE retinal fundus image standard dataset showed an impressive accuracy rate of 97.24%, surpassing traditional filtering methods. Box counting, information, capacity, correlation, and probability dimensions are used in the FD analysis to help us understand the complex and irregular structures of retinal blood vessels. These metrics are valuable for detecting and monitoring retinal diseases in clinical settings. Our comparison with other techniques reveals promising results, particularly in the capacity and information dimensions, with statistical significance (P < 0.05). The potential of fractal dimensions as a screening tool for diabetic retinopathy underscores their importance in epidemiological studies

The real-time Face Recognition (FR) techniques are limited to situations where only one face is visible in close proximity to the camera. Sometimes, the people under surveillance may not be directly facing the camera while walking. However, there is a growing need for FR technology to accurately identify multiple faces in crowded areas even when individuals are not facing the camera. An AI-based multiple-face recognition (MFR) system has been developed to improve performance by incorporating an increased number of pose variation samples. The developed system utilizes a pre-trained FaceNet architecture, which converts the face images into a compact Euclidean space of dimension 128×1. This study focuses on improving accuracy and decreasing computation time for multiple faces within the field of view. Results show that the FaceNet model has a high recognition rate of 99.7%. The system can recognize up to 10 faces in the field of view at a computational time of 1.21 s.

Conventional face recognition (FR) systems face challenges with varying poses, scales, and occlusions, particularly in dense environments where interpersonal occlusion is common. Existing methods using rectangular bounding boxes (BBs) often result in inaccurate detections and lower FR accuracy, particularly when landmark-based alignment fails. To address this, we propose a novel approach integrating Bound YOLO-v7 with a context module to improve face boundary marking, extend the receptive field, and preserve facial contours. Supported by a newly annotated boundary dataset, the method fills the gap in high-quality benchmark data for facial boundary segmentation. In the offline phase, Bound YOLO-v7 extracts face contours, while in the online phase, FaceNet identifies multiple faces in real time. The proposed method achieves a detection rate of 99.83% with mAP values of 0.995 and 0.979 for mAP@0.5 and mAP@0.5:0.95, respectively, and a confidence score of 0.42 ms at 41.3ms. The inclusion of the context module results in mAP@0.5 scores of 99.5% (no occlusion), 96.0% (slight occlusion), and 89.0% (severe occlusion). This approach outperforms the existing method and balances detection accuracy and computational efficiency

Arrhythmia (ARR) and Congestive Heart Failure (CHF) are the most common conditions that have delayed diagnoses in cardiovascular illnesses and the primary cause of death, these are compared with Normal Sinus Rhythm (NSR). Manually interpreting electrocardiogram (ECG) readings can lead to an early identification of various heart diseases. However, because ECG signals have so many different features, manual diagnosis is difficult. Patient lives could be saved with an accurate ARR and CHF group system. The signal classification problem is made simpler by the process of condensing the original signal from an ECG to a much fewer number of characteristics that work together to distinguish between several classes. The variations in variance for each of the three groups in the second-largest scale (second-lowest frequency) wavelet sub-band is examined. It makes use of a quadratic kernel multi-class SVM. This paper deals with two analyses. The whole set of data i.e. training and testing sets to determine the rate of misclassification and confusion matrix. With the best classification accuracy of 97.95%, the SVM divided the raw ECG signal data into three categories: NSR, ARR and CHF. The confusion matrix reveals the misclassification of one class to another i.e. one CHF record as NSR.

Retinal vasculature feature extraction plays a critical role in the diagnosis and treatment of systemic conditions, particularly in the cases of diabetic retinopathy (DR). This research introduces an algorithm that utilizes segmented blood vessels in retinal images to identify and differentiate five stages of DR, including mild, moderate, severe and proliferative. The algorithm effectively extracts retinal blood vessels by integrating morphological operators and matched filters, yielding a more precise output. The algorithm's performance is evaluated using the database IDRiD, demonstrating precision and sensitivity scores comparable to those of a trained observer. A box-counting method was incorporated to measure the fractal dimension (FD) of DR-segmented vessel images at various stages to enhance the accuracy of DR staging. The FD analysis was applied to both thick and thin segments of the blood vessels, enabling the assessment of accuracy, sensitivity and specificity. The results indicate that the algorithm successfully identifies the different stages of DR with an accuracy of 93.65% for the mild stage, of 93.33% for the moderate and severe stages and of 92.71% for proliferative DR compared to the images without DR. The study reveals that the variation in FD between the thick and thin vessel components can be an effective biomarker for identifying the different stages of DR, contributing to a better understanding of disease progression. By combining morphological operators, matched filters and fractal dimension analysis, this research presents a promising approach for specialists involved in diagnosing and treating DR, eventually leading to improved patient care and consequences.

Tremors, involuntary rhythmic oscillations of body parts, can significantly impact individuals' quality of life and pose diagnostic challenges. This study focuses on differentiating among rest tremor, essential tremor, and cerebellar tremor, each associated with distinct neurological pathways and clinical characteristics. Clinicians face considerable challenges due to the similar symptoms exhibited by these tremor types. This paper aims to distinguish the characteristics of these tremors using an advanced algorithm developed with the CVZone library, based on the Mediapipe framework. The developed algorithm differentiates tremors by considering pose variations with 90% accuracy on PT data, 87.5% accuracy on ET data and 85.7% on CT data taken from multiple sources. The binomial test on the results demonstrated the algorithm's capability to differentiate tremors with a statistically significant p-value of 0.00039571, indicating robust performance in correctly identifying tremor types

Breast cancer is one of the deadliest diseases among women ranging from young to old and second common disease that leads to death for women after lung cancer. In this paper investigates the effectiveness of thresholding, edge detection, region-based, and watershed-based segmentation techniques on breast thermal images captured from five distinct perspectives: front view, left at 45°, left at 90°, right at 45° and right at 90° views. The main objective of this research is to identify an appropriate segmentation technique that can improve the accuracy of breast cancer detection in noninvasively through thermal imaging. Each segmentation technique is applied on Region of Interest (ROI) breast thermal images to accurately delineate the breast abnormalities. Results suggest a suitable segmentation that is suitable to analyze breast thermal images with particular angle.

The Process of Grading and Segregation of chicken eggs in various breeds plays a Vital role to assess the standards of eggs that can enable the market to provide Quality eggs to the consumers. The current traditional grading mechanisms are manual and carried on observable traits like shell color and form, that are prone to human mistakes. These manual methods not only make the process cumbersome, but also raises the management cost. To overcome these challenges, this research aimed to differentiate Rhode Island red and white leghorn chicken eggs using non-destructive hyperspectral imaging techniques. Unlike manual inspection or invasive tagging, a nondestructive hyperspectral imaging setup captures a wide range of spectral information from chicken eggs, identifying minor differences in color and texture among different egg breeds in poultry farming. In this experiment, a sample set of 72 Rhode Island red and 72 white leghorn chicken eggs has been tested by using hyperspectral imaging. Spectral features of each breed say Rhode Island red and white leghorn chicken eggs have been identified to differentiate both the breeds.

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An eco-friendly mechanism for disposing of sanitary waste is proposed by the current invention. Sensors, a microcontroller, and an incinerator make up the system. When operated by the microcontroller, the incinerator is set to accept an input signal from an IR Sensor installed inside a dispensing inlet. while it is being controlled by a microcontroller, which is where sanitary waste is distributed. Following that, an input line transports this sanitary waste to a burning chamber. To burn sanitary waste and produce gases and ash, which are then expelled through chimneys and collected in ashtrays, respectively, the waste is placed in a burning chamber. The use of UV light, charcoal, and cotton in chimneys helps to cut down on the discharge of odor-causing gases as well as carbon dioxide. © 2023 IEEE.