Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection.

Automated dental imaging interpretation is one of the most prolific areas of research using artificial intelligence. X-ray imaging systems have enabled dental clinicians to identify dental diseases. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, as well as machine and deep learning models for dental disease diagnoses using X-ray imagery. In this regard, a lightweight Mask-RCNN model is proposed for periapical disease detection. The proposed model is constructed in two parts: a lightweight modified MobileNet-v2 backbone and region-based network (RPN) are proposed for periapical disease localization on a small dataset. To measure the effectiveness of the proposed model, the lightweight Mask-RCNN is evaluated on a custom annotated dataset comprising images of five different types of periapical lesions. The results reveal that the model can detect and localize periapical lesions with an overall accuracy of 94%, a mean average precision of 85%, and a mean insection over a union of 71.0%. The proposed model improves the detection, classification, and localization accuracy significantly using a smaller number of images compared to existing methods and outperforms state-of-the-art approaches.

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.

The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.

Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics.

Patient care and convenience remain the concern of medical professionals and caregivers alike. An unconscious patient confined to a bed may develop fluid accumulation and pressure sores due to inactivity and deficiency of oxygen flow. Moreover, weight monitoring is crucial for an effective treatment plan, which is difficult to measure for bedridden patients. This paper presents the design and development of a smart and cost-effective independent system for lateral rotation, movement, weight measurement, and transporting immobile patients. Optimal dimensions and practical design specifications are determined by a survey across various hospitals. Subsequently, the proposed hoist-based weighing and turning mechanism is CAD-modeled and simulated. Later, the structural analysis is carried out to select suitable metallurgy for various sub-assemblies to ensure design reliability. After fabrication, optimization, integration, and testing procedures, the base frame is designed to mount a hydraulic motor for the actuator, a DC power source for self-sustenance, and lockable wheels for portability. The installation of a weighing scale and a hydraulic actuator is ensured to lift the patient for weight measuring up to 600 pounds or lateral turning of 80 degrees both ways. The developed system offers simple operating characteristics, allows for keeping patient weight records, and assists nurses in changing patients' lateral positions both ways, comfortably massage patients' backs, and transport them from one bed to another. Additionally, being lightweight offers reduced contact with the patient to increase the healthcare staff's safety in pandemics; it is also height adjustable and portable, allowing for use with multiple-sized beds and easy transportation across the medical facility. The feedback from paramedics is encouraging regarding reducing labor-intensive nursing tasks, alleviating the discomfort of long-term bed-ridden patients, and allowing medical practitioners to suggest better treatment plans.

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives.

Artificial intelligence has been widely used in the field of dentistry in recent years. The present study highlights current advances and limitations in integrating artificial intelligence, machine learning, and deep learning in subfields of dentistry including periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology. This article aims to provide a systematic review of current clinical applications of artificial intelligence within different fields of dentistry. The preferred reporting items for systematic reviews (PRISMA) statement was used as a formal guideline for data collection. Data was obtained from research studies for 2009-2022. The analysis included a total of 55 papers from Google Scholar, IEEE, PubMed, and Scopus databases. Results show that artificial intelligence has the potential to improve dental care, disease diagnosis and prognosis, treatment planning, and risk assessment. Finally, this study highlights the limitations of the analyzed studies and provides future directions to improve dental care.

IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition.

This paper presents the design, development, and testing of an IoT-enabled smart stick for visually impaired people to navigate the outside environment with the ability to detect and warn about obstacles. The proposed design employs ultrasonic sensors for obstacle detection, a water sensor for sensing the puddles and wet surfaces in the user's path, and a high-definition video camera integrated with object recognition. Furthermore, the user is signaled about various hindrances and objects using voice feedback through earphones after accurately detecting and identifying objects. The proposed smart stick has two modes; one uses ultrasonic sensors for detection and feedback through vibration motors to inform about the direction of the obstacle, and the second mode is the detection and recognition of obstacles and providing voice feedback. The proposed system allows for switching between the two modes depending on the environment and personal preference. Moreover, the latitude/longitude values of the user are captured and uploaded to the IoT platform for effective tracking via global positioning system (GPS)/global system for mobile communication (GSM) modules, which enable the live location of the user/stick to be monitored on the IoT dashboard. A panic button is also provided for emergency assistance by generating a request signal in the form of an SMS containing a Google maps link generated with latitude and longitude coordinates and sent through an IoT-enabled environment. The smart stick has been designed to be lightweight, waterproof, size adjustable, and has long battery life. The overall design ensures energy efficiency, portability, stability, ease of access, and robust features.

Reduced features set neural network approach based on high-resolution time-frequency images for cardiac abnormality detection.

A suitable temporal and spectral processing of the electrocardiogram (ECG) signals can facilitate the visual interpretation and discrimination between known patterns for classification. This paper proposes a non-invasive hybrid neural network and time-frequency (TF) based method to detect and classify commonly found cardiac abnormalities in ECG signals including congestive heart failure, ventricular tachyarrhythmia, intracardiac atrial fibrillation, arrhythmia, malignant ventricular ectopy, normal sinus rhythm, and postictal heart rate oscillations in partial epilepsy. Non-stationary raw ECG signals are collected from an online healthcare dataset source 'PhysioBank' that contains physiologic signals. These temporal signals are processed through Wigner-Ville distribution to produce high-resolution and concentrated TF images depicting specific visual patterns of cardiac abnormalities. The TF images are used to extract the abnormality parameters with the help of medical experts with good diagnostic accuracy. Principal component analysis (PCA) is employed for feature reduction and important features selection from the ECG signals. The selected features are used for training the multilayer feed-forward artificial neural network (ANN) for detection and classification while training parameters like the number of epochs, activation functions, and the learning rate is suitably selected with appropriate stopping criteria. Experimental results demonstrate the effectiveness of the hybrid neural-TF approach using PCA for abnormality detection and classification.

A Methodological Review of 3D Reconstruction Techniques in Tomographic Imaging.

Computer Vision has provided immense support to medical diagnostics over the past two decades. Analogous to Non Destructive Testing of mechanical parts, advances in medical imaging has enabled surgeons to determine root cause of an illness by consulting medical images particularly 3-D imaging. 3-D modeling in medical imaging has been pursued using surface rendering, volume rendering and regularization based methods. Tomographic reconstruction in 3D is different from camera based scene reconstruction which has been achieved using various techniques including minimal surfaces, level sets, snakes, graph cuts, silhouettes, multi-scale approach, patchwork etc. In tomography limitations of image aquisition method i-e CT Scan, X Rays and MRI as well as non availability of camera parameters for calibration restrict the quality of final reconstruction. In this work, a comprehensive study of related approaches has been carried out with a view to provide a summary of state of the art 3D modeling algorithms developed over the past four decades and also to provide a foundation study for our future work which will include precise 3D reconstruction of human spine.

Idiopathic Radiographic Apical Root Resorption in Wind Instrument Players.

Root resorption of the permanent teeth involves an elaborate interaction among inflammatory cells resulting in loss of dental hard tissues. This report describes three clinical cases where idiopathic root resorption occurred in wind instrument playing patients. These patients produce adequate non-orthodontic forces, while playing their instruments, to expose their teeth to root resorbing force. Careful clinical monitoring of patients' teeth should be undertaken, as the additive effects of orthodontic treatment and musical habits are unknown. CPD/Clinical Relevance: This paper advises that questioning about wind instrument playing during case history-taking would be beneficial to clinicians. Furthermore, careful clinical monitoring of these patients' teeth during orthodontic treatment should be undertaken.

Performance evaluation of diverse T-wave alternans estimators under variety of noise characterizations and alternans distributions.

Prognostic significance of microvolt T-wave alternans (TWA) has been established since their inclusion among important risk stratifiers for sudden cardiac death. Signal processing schemes employed for TWA estimation have their peculiar theoretical assumptions and reported statistics. An unbiased comparison of all these techniques is still a challenge. Choosing three classical schemes, this study aims to achieve holistic performance evaluation of diverse TWA estimators from a three dimensional standpoint, i.e., estimation statistics, alternan distribution and ECG signal quality. Three performance indices called average deviation (ϑ( L )), moment of deviation (ϑ( m )) and coefficient of deviation ([Formula: see text]) are devised to quantify estimator performance and consistency. Both synthetic and real physiological noises, as well as variety of temporal distributions of alternan waveforms are simulated to evaluate estimators' responses. Results show that modification of original estimation statistics, consideration of relevant noise models and a priori knowledge of alternan distribution is necessary for an unbiased performance comparison. Spectral method proves to be the most accurate for stationary TWA, even at SNRs as low as 5 dB. Correlation method's strength lies in accurately detecting temporal origins of multiple alternan episodes within a single analysis window. Modified moving average method gives best estimation at lower noise levels (SNR >25 dB) for non-stationary TWA. Estimation of both MMAM and CM is adversely effected by even small baseline drifts due to respiration, although CM gives considerably higher deviation levels than MMAM. Performance of SM is only effected when fundamental frequency of baseline drift due to respiration falls within the estimation band around 0.5 cpb.

Fluoride varnish reduces white spot lesions during orthodontic treatment.

DESIGN: This was a randomised controlled trial (RCT) set in a community dental practice. INTERVENTION: The test varnish was a commercially available product, Fluor Protector (Ivoclar Vivadent, Schaan, Liechtenstein), containing 0.1% fluoride as difluorosilane in a polyurethane varnish base. The placebo varnish applied had an identical composition but without fluoride. OUTCOME MEASURE: The incidence and prevalence of white spot lesions (WSL) on the upper incisors, cuspids and premolars were recorded, as scored from digital photographs by two independent examiners. In the case of disagreement, cases were re-examined until a consensus was achieved. RESULTS: The incidence of WSL during the treatment period was 7.4% in the fluoride varnish group compared with 25.3% placebo group (P <0.001). The mean progression score was significantly lower in the fluoride varnish group than in the placebo group, (0.8 +/- 2.0 vs 2.6 +/- 2.8; P <0.001). The absolute risk reduction was 18% and the number-needed-to-treat was calculated to be 5.5 (95% confidence interval, 3.7-10.9). CONCLUSIONS: The results strongly suggest that regular topical fluoride varnish applications may reduce the development of WSL adjacent to the bracket base during treatment with fixed appliances.

No evidence to support one surgical technique over the other for the management of palatally displaced canines.

DATA SOURCES: Medline, Embase, the Cochrane Central Register of Controlled Trials and the Cochrane Oral Health Group's Trials Register were searched with no restrictions over publication status or language. STUDY SELECTION: Studies chosen were those where participants received surgical treatment to correct upper palatally impacted canines. There was no restriction for age, presenting malocclusion or the type of active orthodontic treatment undertaken. Unilateral and bilaterally displaced canines were included but trials with participants who had craniofacial deformity/ syndrome were excluded. DATA EXTRACTION AND SYNTHESIS: Two review authors independently and in duplicate assessed and selected studies. The Cochrane Collaboration statistical guidelines were to be followed for data synthesis. RESULTS: No studies were identified that met the inclusion criteria. CONCLUSIONS: Currently, there is no evidence to support one surgical technique over the other in terms of dental health, aesthetics, economics and patient factors. Until high quality clinical trials are conducted with participants randomly allocated into the two treatment groups, methods of exposing canines will be left to the personal choice of the surgeon and orthodontist.