Hair and Scalp Disease Detection using Machine Learning and Image Processing

Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair loss and regular hair fall. Diagnosing hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Because of that, the overall diagnosis gets delayed, which worsens the severity of the illness. Due to the image-processing ability, neural network-based applications are used in various sectors, especially healthcare and health informatics, to predict deadly diseases like cancers and tumors. These applications assist clinicians and patients and provide an initial insight into early-stage symptoms. In this study, we used a deep learning approach that successfully predicts three main types of hair loss and scalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited study in this area, unavailability of a proper dataset, and degree of variety among the images scattered over the internet made the task challenging. 150 images were obtained from various sources and then preprocessed by denoising, image equalization, enhancement, and data balancing, thereby minimizing the error rate. After feeding the processed data into the 2D convolutional neural network (CNN) model, we obtained overall training accuracy of 96.2%, with a validation accuracy of 91.1%. The precision and recall score of alopecia, psoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also created a dataset of the scalp images for future prospective researchers

Essential nursing care for the sole

This paper illuminates the transformational journey experienced while exploring nursing practice involvement in essential holistic foot care as a Doctor of Nursing-Transcultural Nursing (DNP-TCN) practice project. The underlying theoretical framework is based on the relational caring complexity theory by Marilyn A. Ray and Marian C. Turkel, depicting model complexity concepts essential to holistic health and wellness nursing foot cares. As an advanced practice nurse, this practice shift is an assertion of health and wellness essentials in the model of holistic nursing, as foot health is tied to the health of the physical body and spiritual soul of the living. Individual foot care propels complex choices arising from intricately interconnected patterns forming the foundation of our lives as our feet transport us from one moment and one experience to the next. This final practice project aspires to convey the significance of essential holistic nursing foot care, within the culture of nursing, in facilitating a healthy, multidimensional life of an individual. Our feet form the foundation of what transport us from one experience, one paradigm, to the next. When we lose any fragment of meaning our feet provide, this influences and confounds every facet of our lives. As aspects of the relational caring complexity theory of Ray Turkel, outcomes of this journey go beyond basic holistic nursing foot care shaping the character of this nurse’s transition from DNP student into participatory practitioner and novice author

Deep Learning Methods for Tooth Detection and Classification in Various Dental Image Datasets: A Taxonomy and Future Directions

Deep learning approaches have made significant advancements in recent years, generating considerable interest in using them for medical image analysis. In dentistry, the precision of tooth detection and classification serves as the cornerstone of dental practice as it can identify the presence of dental abnormalizes at an early stage. This paper presents an exploration of the potential of deep learning methods for tooth detection and classification across a variety of dental imaging datasets including radiographs, cone-beam computed tomography (CBCT) scans, and photograph images. Convolutional Neural Networks (CNNs) have emerged as one of the most widely used and effective deep learning methods in the field of dental disease diagnosis and medical image analysis. The study aims to conceptualize how these models can effectively learn intricate tooth features, despite having variations in tooth morphology, image quality, and imaging techniques. It highlights the increasing role of deep learning in diagnosing dental diseases and emphasizes the importance of accurate tooth classification for effective treatment planning. The study reviews existing research in deep learning-based tooth classification, discusses challenges including dataset scarcity and model interpretability, and suggests future directions

Integrating Evolutionary Genetics to Medical Genomics: Evolutionary Approaches to Investigate Disease-Causing Variants

In recent years, next-generation sequencing (NGS) platforms that facilitate generation of a vast amount of genomic variation data have become widely used for diagnostic purposes in medicine. However, identifying the potential effects of the variations and their association with a particular disease phenotype is the main challenge in this field. Several strategies are used to discover the causative mutations among hundreds of variants of uncertain significance. Incorporating information from healthy population databases, other organisms’ databases, and computational prediction tools are evolution-based strategies that give valuable insight to interpret the variant pathogenicity. In this chapter, we first provide an overview of NGS analysis workflow. Then, we review how evolutionary principles can be integrated into the prioritization schemes of analyzed variants. Finally, we present an example of a real-life case where the use of evolutionary genetics information facilitated the discovery of disease-causing variants in medical genomics

AI Enabled Drug Design and Side Effect Prediction Powered by Multi-Objective Evolutionary Algorithms & Transformer Models

Due to the large search space and conflicting objectives, drug design and discovery is a difficult problem for which new machine learning (ML) approaches are required. Here, the problem is to invent a method by which new, therapeutically useful, compounds can be discovered; and to simultaneously avoid compounds which will fail clinical trials or pass unwanted effects onto the end patient. By extending current technologies as well as adding new ones, more design criteria can be included, and more promising novel drugs can be discovered. This work advances the field of computational drug design by (1) developing MOEA-DT, a non-deep learning application for multi-objective molecular optimization, which generates new molecules with high performance in a variety of design criteria; and (2) developing SEMTL-BERT, a side effect prediction algorithm which leverages the latest ML techniques and datasets to accomplish its task. Experiments performed show that MOEA-DT either matches or outperforms other similar methods, and that SEMTL-BERT can enhance predictive ability