Understanding deep learning - challenges and prospects

The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and will improve the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. The current narrative review was planned to make comprehension of Artificial Intelligence algorithms relatively straightforward. The focus was planned to be kept on the current developments and prospects of Artificial Intelligence in dentistry, especially Deep Learning and Convolutional Neural Networks in diagnostic imaging. The narrative review may facilitate the interpretation of seemingly perplexing research published widely in dental journals

An overview of the epidemiology, transmission, pathogenesis and treatment of scabies

Summary: Scabies is a well-known, yet a poorly understood neglected tropical disease (NTD). Although less common in the UK, scabies epidemics regularly occur abroad, in tropical, less developed communities (LDCs). Cases are prevalent in communities which tend to live with overcrowding, poor sanitation and limited access to healthcare facilities and medication; this environment provides the perfect breeding ground for the growth and the transmission of scabies. The body has a delayed response to infestation, this is due to the scabies mite’s ability to disrupt the complement cascade and delay the onset of the adaptive arm of the immune response Relevance: Contrary to popular belief, anyone can become infested with scabies. Although not usually life-threatening, scabies can cause unpleasant symptoms, as well as worsen existing skin conditions, which can reduce a person’s quality of life. Prompt diagnosis is challenging in LDCs. When failed to be diagnosed, scabies may lead to serious complications such as secondary skin sepsis, as well as allowing further transmission. Scabies is highly contagious; clinicians should be aware how to spot and treat scabies early on, and additionally know to offer treatment to other members that the patient has been in close contact with. Take Home Messages: Management for scabies is relatively simple and involves the application of topical medication, such as Permethrin. Despite this, there are still many barriers to treating epidemics in LDCs, such as a lack of access to treatment and healthcare professionals, a lack of awareness from clinicians about the condition’s clinical manifestations, as well as lack of infrastructure to definitively diagnose the condition. Despite progress in management of the condition, the pathophysiology and transmission of the condition are only partly understood, and the rise of resistance to current scabicides is indicative of the need for newer treatments, especially within resource poor communities

Application of deep learning in teeth identification tasks on panoramic radiographs

Objectives: To investigate the current developments of Artificial Intelligence (AI) in teeth identification on Panoramic Radiographs (PR). Our aim was to evaluate and compare the performances of Deep Learning (DL) models that have been employed in the execution of this task. Methods: The systematic review was registered on PROSPERO. All recent studies that utilized DL models for identifying teeth on PRs were included in this review. An extensive search of the medical electronic databases including PubMed NLM, EBSCO Dentistry & Oral Sciences Source, and Wiley Cochrane Library was conducted. This was followed by a hand search of the IEEE Xplore database. The diagnostic performance of DL models in teeth identification tasks on PR was the primary outcome assessed in this review. The risk of bias assessment of the included studies was evaluated via the modified QUADAS-2 tool. Owing to the heterogeneity of the reported performance metrics, a meta-analysis was not possible.. Results: The search yielded a total of 282 articles, out of which 13 relevant ones were included in this review. These studies utilized a diverse range of DL models for teeth identification tasks on PRs and reported their performances using a variety of metrics. Conclusion: The results of teeth identification tasks carried out by DL models are encouraging; however, there is a need for the shortcomings that have been identified in our preliminary review, to be addressed by future researcher

Implementation of transfer learning for the segmentation of human mesenchymal stem cells-A validation study

Introduction: Stem cell therapy has been gaining interest in the regeneration rather than repair of lost human tissues. However, the manual analysis of stem cells prior to implantation is a cumbersome task that can be automated to improve the efficiency and accuracy of this process.Objective: To develop a Deep Learning (DL) algorithm for segmentation of human mesenchymal stem cells (MSCs) on micrographic images and to validate its performance relative to the ground truth laid down via annotation.Methodology: Pre-trained DeepLab algorithms were trained on annotated images of human MSCs obtained from the open-source EVICAN dataset. This dataset comprises of partially annotated images; a limitation that is overcome by blurring backgrounds of these images which consequently blurs the unannotated cells. Two algorithms were trained on the two different kinds of images from this dataset; with blurred and normal backgrounds, respectively. Algorithm 1 was trained on 139 images with blurred backgrounds and algorithm 2 was trained on 37 images from the same dataset with normal backgrounds to replicate real-life scenarios.Results: The performance metrics of algorithm 1 included accuracy of 99.22%, dice co-efficient of 99.66% and Intersection over Union (IoU) score of 0.84. Algorithm 2 was 96.34% accurate with dice co-efficient and IoU scores of 98.39% and 0.48, respectively.Conclusion: Both algorithms showed adequate performance in the segmentation of human MSCs with performance metrics close to the ground truth. However, algorithm 2 has better clinical applicability, even with smaller dataset and relatively lower performance metrics

The Dilemma Of Management Of Cystic Lesions; An Uncertain Way Forward: A Case Report

Periapical diseases ranges from mild granulomatous lesions to large cystic ones, with the treatments corresponding to their respective pre-operative diagnoses. However, the determination of cause of periapical radiolucency is impossible on pre-operative clinical and radiographic examinations. We present a case highlighting the difficulties encountered in treating a periapical cyst using the current evidence in literature. It demonstrates the uncertainty involved in treating such lesions, owing to the impossible nature of determining the histopathological nature of the cyst, i.e., being either true cysts or pocket cysts. This case includes orthograde re-treatment; decompression of the cystic lesion, followed by peri-apical surgery of two teeth over a course of three years; and the uncertain outcomes encountered after each phase of the treatment