Skin Cancer Classification Using Deep Spiking Neural Network.

Skin cancer is one of the primary causes of death globally, and experts diagnose it by visual inspection, which can be inaccurate. The need for developing a computer-aided method to aid dermatologists in diagnosing skin cancer is highlighted by the fact that early identification can lower the number of deaths caused by skin malignancies. Among computer-aided techniques, deep learning is the most popular for identifying cancer from skin lesion images. Due to their power-efficient behavior, spiking neural networks are attractive deep neural networks for hardware implementation. We employed deep spiking neural networks using the surrogate gradient descent method to classify 3670 melanoma and 3323 non-melanoma images from the ISIC 2019 dataset. We achieved an accuracy of 89.57% and an F1 score of 90.07% using the proposed spiking VGG-13 model, which is higher than the VGG-13 and AlexNet using less trainable parameters.

Use of Sensors for Fall Prediction in Older Persons: A Scoping Review.

The application of technological advances and clear articulation of how they improve patient outcomes are not always well described in the literature. Our research team investigated the numerous ways to measure conditions and behaviors that precede patient events and could signal an important change in health through a scoping review. We searched for evidence of technology use in fall prediction in the population of older adults in any setting. The research question was described in the population-concept-context format: "What types of sensors are being used in the prediction of falls in older persons?" The purpose was to examine the numerous ways to obtain continuous measurement of conditions and behaviors that precede falls. This area of interest may be termed emerging knowledge . Implications for research include increased attention to human-centered design, need for robust research trials that clearly articulate study design and outcomes, larger sample sizes and randomization of subjects, consistent oversight of institutional review board processes, and elucidation of the human costs and benefits to health and science.

Rethinking Skin Lesion Segmentation in a Convolutional Classifier.

Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can improve diagnostic accuracy and save lives. CNNs have been successfully used in both skin lesion segmentation and classification. For reasons heretofore unclear, previous works have found image segmentation to be, conflictingly, both detrimental and beneficial to skin lesion classification. We investigate the effect of expanding the segmentation border to include pixels surrounding the target lesion. Ostensibly, segmenting a target skin lesion will remove inessential information, non-lesion skin, and artifacts to aid in classification. Our results indicate that segmentation border enlargement produces, to a certain degree, better results across all metrics of interest when using a convolutional based classifier built using the transfer learning paradigm. Consequently, preprocessing methods which produce borders larger than the actual lesion can potentially improve classifier performance, more than both perfect segmentation, using dermatologist created ground truth masks, and no segmentation altogether.

Natural language processing of online support group postings reveals patients' perspectives on strategies for managing psoriasis.

Psoriasis is a chronic skin disorder, and patients encounter high physical and psychosocial burdens. Social media forums feature extensive patient-generated comments. We hypothesized that analyzing patient-posted comments using natural language processing would provide insights into patient engagements, sentiments, concerns, and support, which are vital for the holistic management of psoriasis. We collected 32,000 active user comments posted on Reddit. We applied Latent Dirichlet Allocation to categorize posts into popular topics and employed spectral clustering to establish cohesive themes and word representation frequency within these topics. We sorted posts into 29 significant topics of discussion and categorized them into four categories: management (37.48%), emotion (21.57%), presentation (19.79%), and others (3.57%). The frequent posts on management were diet (7.23%), biologics (6.95%), and adverse effects (3.88%). The emotion category comprised negative sentiments (11.02%), encouragement (5.49%), and gratitude (5.06%). The presentation topic included a discussion of scalp (5.69%), flare-timing (3.63%), and arthritis (2.64%). Others comprised differential diagnosis (5.01%), leaky gut (4.12%), and referrals (3.70%). This study identified patients' experiences and perspectives associated with psoriasis, which should be considered to tailor support systems to improve their quality of life.

Machine learning for predicting opioid use disorder from healthcare data: A systematic review.

INTRODUCTION: The US opioid epidemic has been one of the leading causes of injury-related deaths according to the CDC Injury Center. The increasing availability of data and tools for machine learning (ML) resulted in more researchers creating datasets and models to help analyze and mitigate the crisis. This review investigates peer-reviewed journal papers that applied ML models to predict opioid use disorder (OUD). The review is split into two parts. The first part summarizes the current research in OUD prediction with ML. The second part evaluates how ML techniques and processes were used to achieve these results and suggests improvements to refine further attempts to use ML for OUD prediction. METHODS: The review includes peer-reviewed journal papers published on or after 2012 that use healthcare data to predict OUD. We searched Google Scholar, Semantic Scholar, PubMed, IEEE Xplore, and Science.gov in September of 2022. Data extracted includes the study's goal, dataset used, cohort selected, types of ML models created, model evaluation metrics, and the details of the ML tools and techniques used to create the models. RESULTS: The review analyzed 16 papers. Three papers created their dataset, five used a publicly available dataset, and the remaining eight used a private dataset. Cohort size ranged from the low hundreds to over half a million. Six papers used one type of ML model, and the remaining ten used up to five different ML models. The reported ROC AUC was higher than 0.8 for all but one of the papers. Five papers used only non-interpretable models, and the other 11 used interpretable models exclusively or in combination with non-interpretable ones. The interpretable models were the highest or second-highest ROC AUC values. Most papers did not sufficiently describe the ML techniques and tools used to produce their results. Only three papers published their source code. CONCLUSIONS: We found that while there are indications that ML methods applied to OUD prediction may be valuable, the lack of details and transparency in creating the ML models limits their usefulness. We end the review with recommendations to improve studies on this critical healthcare subject.

Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline.

Cognitive impairment is an age-associated disorder of increasing prevalence as the aging population continues to grow. Classified based on the level of cognitive decline, memory, function, and capacity to conduct activities of daily living, cognitive impairment ranges from mild cognitive impairment to dementia. When considering the insidious nature of the etiologies responsible for varying degrees of cognitive impairment, early diagnosis may provide a clinical benefit through the facilitation of early treatment. Typical diagnosis relies heavily on evaluation in a primary care setting. However, there is evidence that other diagnostic tools may aid in an earlier diagnosis of the different underlying pathologies responsible for cognitive impairment. Artificial intelligence represents a new intersecting field with healthcare that may aid in the early detection of neurodegenerative disorders. When assessing the role of AI in detecting cognitive decline, it is important to consider both the diagnostic efficacy of AI algorithms and the clinical relevance and impact of early interventions as a result of early detection. Thus, this review highlights promising investigations and developments in the space of artificial intelligence and healthcare and their potential to impact patient outcomes.

Skin Cancer Detection Using Deep Learning-A Review.

Skin cancer is one the most dangerous types of cancer and is one of the primary causes of death worldwide. The number of deaths can be reduced if skin cancer is diagnosed early. Skin cancer is mostly diagnosed using visual inspection, which is less accurate. Deep-learning-based methods have been proposed to assist dermatologists in the early and accurate diagnosis of skin cancers. This survey reviewed the most recent research articles on skin cancer classification using deep learning methods. We also provided an overview of the most common deep-learning models and datasets used for skin cancer classification.

Treatment options and emotional well-being in patients with rosacea: An unsupervised machine learning analysis of over 200,000 posts.

Background: Many patients with rosacea join online support groups to gather and disseminate information about disease management and provide emotional support for others. Objective: To better understand rosacea patient's primary concerns for the disease as well as their disease search patterns online. Methods: Overall, 207,038 posts by 41,400 users were collected from June 1, 2017, to June 1, 2022, in a popular online forum. We applied Latent Dirichlet Allocation (LDA), an unsupervised machine learning model, to organize the posts into topics. Keywords for each topic supplied by LDA were used to manually assign topic and category labels. Results: Twenty-three significant topics of conversation were identified and organized into 4 major categories, including Management (50.33%), Clinical Presentation (24.14%), Emotion (21.97%), and Information Appraisal (3.57%). Limitations: Although we analyzed the largest forum on the internet for rosacea, generalizability is limited given the presence of other smaller forums and the skewed demographics of forum users. Conclusion: Social media forums play an important role for disease discussion and emotional venting. Although rosacea management was the most frequently discussed topic, emotional posting was a significantly prevalent occurrence.

Assessing Methods and Tools to Improve Reporting, Increase Transparency, and Reduce Failures in Machine Learning Applications in Health Care.

Artificial intelligence applications for health care have come a long way. Despite the remarkable progress, there are several examples of unfulfilled promises and outright failures. There is still a struggle to translate successful research into successful real-world applications. Machine learning (ML) products diverge from traditional software products in fundamental ways. Particularly, the main component of an ML solution is not a specific piece of code that is written for a specific purpose; rather, it is a generic piece of code, a model, customized by a training process driven by hyperparameters and a dataset. Datasets are usually large, and models are opaque. Therefore, datasets and models cannot be inspected in the same, direct way as traditional software products. Other methods are needed to detect failures in ML products. This report investigates recent advancements that promote auditing, supported by transparency, as a mechanism to detect potential failures in ML products for health care applications. It reviews practices that apply to the early stages of the ML lifecycle, when datasets and models are created; these stages are unique to ML products. Concretely, this report demonstrates how two recently proposed checklists, datasheets for datasets and model cards, can be adopted to increase the transparency of crucial stages of the ML lifecycle, using ChestX-ray8 and CheXNet as examples. The adoption of checklists to document the strengths, limitations, and applications of datasets and models in a structured format leads to increased transparency, allowing early detection of potential problems and opportunities for improvement. Keywords: Artificial Intelligence, Machine Learning, Lifecycle, Auditing, Transparency, Failures, Datasheets, Datasets, Model Cards Supplemental material is available for this article. © RSNA, 2022.

Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum.

Artificial intelligence (AI) is on course to become a mainstay in the patient's room, physician's office, and the surgical suite. Current advancements in health care technology might put future physicians in an insufficiently equipped position to deal with the advancements and challenges brought about by AI and machine learning solutions. Physicians will be tasked regularly with clinical decision-making with the assistance of AI-driven predictions. Present-day physicians are not trained to incorporate the suggestions of such predictions on a regular basis nor are they knowledgeable in an ethical approach to incorporating AI in their practice and evolving standards of care. Medical schools do not currently incorporate AI in their curriculum due to several factors, including the lack of faculty expertise, the lack of evidence to support the growing desire by students to learn about AI, or the lack of Liaison Committee on Medical Education's guidance on AI in medical education. Medical schools should incorporate AI in the curriculum as a longitudinal thread in current subjects. Current students should understand the breadth of AI tools, the framework of engineering and designing AI solutions to clinical issues, and the role of data in the development of AI innovations. Study cases in the curriculum should include an AI recommendation that may present critical decision-making challenges. Finally, the ethical implications of AI in medicine must be at the forefront of any comprehensive medical education.

Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes.

BACKGROUND: As medicine and the delivery of healthcare enters the age of Artificial Intelligence (AI), the need for competent human-machine interaction to aid clinical decisions will rise. Medical students need to be sufficiently proficient in AI, its advantages to improve healthcare's expenses, quality, and access. Similarly, students must be educated about the shortfalls of AI such as bias, transparency, and liability. Overlooking a technology that will be transformative for the foreseeable future would place medical students at a disadvantage. However, there has been little interest in researching a proper method to implement AI in the medical education curriculum. This study aims to review the current literature that covers the attitudes of medical students towards AI, implementation of AI in the medical curriculum, and describe the need for more research in this area. METHODS: An integrative review was performed to combine data from various research designs and literature. Pubmed, Medline (Ovid), GoogleScholar, and Web of Science articles between 2010 and 2020 were all searched with particular inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were successively pooled together, recorded, and analyzed quantitatively using a modified Hawkings evaluation form. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was utilized to help improve reporting. RESULTS: A total of 39 articles meeting inclusion criteria were identified. Primary assessments of medical students attitudes were identified (n = 5). Plans to implement AI in the curriculum for the purpose of teaching students about AI (n = 6) and articles reporting actual implemented changes (n = 2) were assessed. Finally, 26 articles described the need for more research on this topic or calling for the need of change in medical curriculum to anticipate AI in healthcare. CONCLUSIONS: There are few plans or implementations reported on how to incorporate AI in the medical curriculum. Medical schools must work together to create a longitudinal study and initiative on how to successfully equip medical students with knowledge in AI.

Artificial intelligence and COVID-19: A multidisciplinary approach.

The COVID-19 pandemic is taking a colossal toll in human suffering and lives. A significant amount of new scientific research and data sharing is underway due to the pandemic which is still rapidly spreading. There is now a growing amount of coronavirus related datasets as well as published papers that must be leveraged along with artificial intelligence (AI) to fight this pandemic by driving news approaches to drug discovery, vaccine development, and public awareness. AI can be used to mine this avalanche of new data and papers to extract new insights by cross-referencing papers and searching for patterns that AI algorithms could help discover new possible treatments or help in vaccine development. Drug discovery is not a trivial task and AI technologies like deep learning can help accelerate this process by helping predict which existing drugs, or brand-new drug-like molecules could treat COVID-19. AI techniques can also help disseminate vital information across the globe and reduce the spread of false information about COVID-19. The positive power and potential of AI must be harnessed in the fight to slow the spread of COVID-19 in order to save lives and limit the economic havoc due to this horrific disease.

Neural network approach to background modeling for video object segmentation.

This paper presents a novel background modeling and subtraction approach for video object segmentation. A neural network (NN) architecture is proposed to form an unsupervised Bayesian classifier for this application domain. The constructed classifier efficiently handles the segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed NN serve as a model of the background and are temporally updated to reflect the observed statistics of background. The segmentation performance of the proposed NN is qualitatively and quantitatively examined and compared to two extant probabilistic object segmentation algorithms, based on a previously published test pool containing diverse surveillance-related sequences. The proposed algorithm is parallelized on a subpixel level and designed to enable efficient hardware implementation.

Co.Vi.Wo.: Color Visual Words Based on Non-Predefined Size Codebooks.

Due to the rapid development of information technology and the continuously increasing number of available multimedia data, the task of retrieving information based on visual content has become a popular subject of scientific interest. Recent approaches adopt the bag-of-visual-words (BOVW) model to retrieve images in a semantic way. BOVW has shown remarkable performance in content-based image retrieval tasks, exhibiting better retrieval effectiveness over global and local feature (LF) representations. The performance of the BOVW approach depends strongly, however, on predicting the ideal codebook size, a difficult and database-dependent task. The contribution of this paper is threefold. First, it presents a new technique that uses a self-growing and self-organized neural gas network to calculate the most appropriate size of a codebook for a given database. Second, it proposes a new soft-weighting technique, whereby each LF is classified into only one visual word (VW) with a degree of participation. Third, by combining the information derived from the method that automatically detects the number of VWs, the soft-weighting method, and a color information extraction method from the literature, it shapes a new descriptor, called color VWs. Experimental results on two well-known benchmarking databases demonstrate that the proposed descriptor outperforms 15 contemporary descriptors and methods from the literature, in terms of both precision at K and its ability to retrieve the entire ground truth.