Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19.

With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach.

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.

Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. 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, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

Same-Day Discharge After Left Bundle Area Pacing.

BACKGROUND: Left bundle area pacing (LBAP) has emerged as an area that appears to be an attractive alternative to other forms of physiological pacing owing to its ease and favorable pacing parameters. Same-day discharge after conventional pacemakers, implantable cardioverter defibrillators, and more recently leadless pacemakers have become routine, especially after the COVID-19 pandemic. With the advent of LBAP, the safety and feasibility of same-day discharge remain unclear. METHODS: This is a retrospective, observational case series of consecutive, sequential patients undergoing LBAP at Baystate Medical Center, an academic teaching hospital. We included all patients who underwent LBAP and were discharged on the same day of procedure completion. Safety parameters included any procedure-related complications including pneumothorax, cardiac tamponade, septal perforation, and lead dislodgement. Pacemaker parameters included pacing threshold, R-wave amplitude, and lead impedance pre-discharge the following day of implantation and up to 6 months of follow-up. RESULTS: A total of 11 patients were included in our analysis, the average age was 70.3 ± 6.74 years. The most common indication for pacemaker insertion was AV block (73%). No complications were seen in any of the patients. The average time between the procedure and discharge was 5.6 hours. Pacemaker and lead parameters were stable after 6 months of follow-up. CONCLUSIONS: In this case series, we find that same-day discharge after LBAP for any indication is a safe and feasible option. As this mode of pacing becomes increasingly more common, larger prospective studies evaluating the safety and feasibility of early discharge after LBAP will be needed.

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.

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.

Death among patients hospitalized with symptomatic COVID-19: Implications for high-risk patients.

BACKGROUND: We aimed to examine the role played by the COVID-19 infection in patients' death and to determine the proportion of patients for whom it was a major contributor to death. METHODS: We included patients ≥50 years old who were hospitalized with COVID-19 infection and died between March 1, 2020 and September 30, 2020 in a tertiary medical center. We considered COVID-19 infection to be a major cause for death if the patient had well-controlled medical conditions and death was improbable without coronavirus infection, and a minor cause for death if the patient had serious illnesses and had an indication for palliative care. RESULTS: Among 243 patients, median age was 80 (interquartile intervals: 72-86) and 40% were female. One in two had moderate or severe frailty and 41% had dementia. Nearly 60% of the patients were classified as having advanced, serious illnesses present prior to the hospitalization, with death being expected within 12 months, and among this group 39% were full code at admission. In the remaining 40% of patients, deaths were classified as unexpected based on patients' prior conditions, suggesting that COVID-19 infection complications were the primary contributor to death. CONCLUSIONS: For slightly less than half (40%) of patients who died of complications of COVID-19, death was an unexpected event. Among the 60% of patients for whom death was not a surprise, our findings identify opportunities to improve end-of-life discussions and implement shared decision-making in high-risk patients early on or prior to hospitalization.

16S metagenomics dataset of Zea mays and Triticum aestivum rhizosphere from Kallar Syedan Punjab, Pakistan.

Plant microbiome referred to as plant second genome, plays pivotal role in determination of vigor and productivity of plant. Current high-throughput sequence technologies provide remarkable insight into microbial diversity and host microbe interaction. The obtained dataset aimed to reveal the core bacterial community residing the rhizosphere of two leading cereal crops Zea mays and Triticum aestivum grown in different seasons at the same geographical area. The rhizosphere bacterial communities were explored via amplicon sequencing of V3-V4 region of 16S rRNA region using IonS5™XL sequencing platform. The classified tags for 16S rRNA from both the samples were clustered into 1502 Microbial operational taxonomic units (OTUs) at 97% similarity with 1340 OTUs in Zea mays and 1337 OTUs in Triticum aestivum. Ten bacterial phyla predominant in the rhizosphere were Proteobacteria, Actinobacteria, Firmicutes, Acidobacteria, Bacteroidetes, Chloroflexi, Gemmatimonadetes, Verrucomicrobia, Nitrospirae and Thermomicrobia. These bacterial phyla accounted for 98% and 98.9% of the OTUs in Zea mays and Triticum aestivum, respectively. Statistical analysis depicted the presence of slight variations in the relative abundance of bacterial groups residing the rhizosphere of Zea mays and Triticum aestivum. The community data produced in the present work can be used for meta-analysis studies to understand rhizosphere bacterial community of two major cereal crops. Furthermore, bacterial composition and diversity data is prerequisite for rhizosphere engineering to enhance cereal production to cope with upcoming global challenges of climate change and population growth.

Abruptio Placentae with Type II Respiratory Failure Secondary to Acute Interstitial Pneumonia Responsive to Steroids.

Acute Interstitial Pneumonia (AIP) is categorized as Idiopathic Interstitial Pneumonia (IIP), in which the cause is unknown. Ayoung female of 22 years presented in 34 weeks gestation with abruptio placentae (AP) and underwent Lower Segment Caesarian Section (LSCS) for AP. It progressed to type II respiratory failure secondary to AIPon 4th day post-surgery. It remained unresponsive when treated with noninvasive ventilation (NIV-BiPAP) along with antibiotics. Later, a trial treatment of pulse therapy of Methylprednisolone was executed on 7th day post-surgery which resulted in dramatic improvement in symptoms. It is uncommon to have type II respiratory failure secondary to AIP, and it is rarely steroid responsive.