Cognitive Technologies: Machine Learning, Artificial Intelligence, and Convolutional Neural Networks in Computer Vision

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Background: Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. Key Message: In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. Summary: This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

MP07-06 EVALUATING THE EFFICACY OF ARTIFICIAL INTELLIGENCE IN PREDICTING OUTCOMES FOR PEDIATRIC HYDRONEPHROSIS: A SYSTEMATIC REVIEW

IntroductionThe utility of non-contact technologies for screening infectious diseases such as COVID-19 can be enhanced by improving the underlying Artificial Intelligence (AI) models and integrating them into data visualization frameworks. AI models that are a fusion of different Machine Learning (ML) models where one has leveraged the different positive attributes of these models have the potential to perform better in detecting infectious diseases such as COVID-19. Furthermore, integrating other patient data such as clinical, socio-demographic, economic and environmental variables with the image data (e.g., chest X-rays) can enhance the detection capacity of these models.MethodsIn this study, we explore the use of chest X-ray data in training an optimized hybrid AI model based on a real-world dataset with limited sample size to screen patients with COVID-19. We develop a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) model based on image features extracted through a CNN and EfficientNet B0 Transfer Learning Model and applied to an RF classifier. Our approach includes an intermediate step of using the RF's wrapper function, the Boruta Algorithm, to select important variable features and further reduce the number of features prior to using the RF model.Results and discussionThe new model obtained an accuracy and recall of 96% for both and outperformed the base CNN model and four other experimental models that combined transfer learning and alternative options for dimensionality reduction. The performance of the model fares closely to relatively similar models previously developed, which were trained on large datasets drawn from different country contexts. The performance of the model is very close to that of the “gold standard” PCR tests, which demonstrates the potential for use of this approach to efficiently scale-up surveillance and screening capacities in resource limited settings.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

Nailfold capillaroscopy (NFC) is a well-established, highly specialized, non-invasive method for assessing microcirculation and represents the “gold standard” in rheumatology for distinguishing primary from secondary Raynaud’s phenomenon (RP). Through analysis of capillary structures in the nailfold region, clinicians can identify diff erent patterns (scleroderma, scleroderma-like, and non-specifi c). Despite its clinical signifi cance, image interpretation remains challenging due to subjectivity and the requirement for substantial examiner expertise. In recent years, artifi cial intelligence (AI) has off ered novel solutions through the automated recognition and analysis of capillary structures. Machine learning and deep learning approaches have demonstrated high eff ectiveness in detecting abnormalities, classifying capillary patterns, and predicting the risk of systemic sclerosis. Projects such as CAPI-Detect, along with various other machine learning, deep learning, and neural network models (DenseNet-121, Effi cientNet-B0, ResNet-34, NFC-Net), have shown that AI can identify novel quantitative parameters not accessible through traditional visual assessment and can increase the objectivity and reproducibility of results up to 90%. Most studies in this fi eld focus on capillaroscopic patterns in systemic sclerosis (SSc); however, pilot studies in juvenile dermatomyositis, diabetes mellitus, and hypertension further highlight the applicability of these technologies. Some AI models are even capable of distinguishing onychomycosis, nail psoriasis, and subungual melanoma. Machine learning and deep learning models based on image analysis (Vision Transformer, ViT) appear to represent an additional valid system for the early and rapid interpretation of NFC images and morphological biomarkers in systemic sclerosis (SSc), integrating EULAR-validated algorithms for distinguishing scleroderma from non-scleroderma patterns with artifi cial intelligence (AI). Beyond its diagnostic applications, AI also holds potential in disease monitoring, assessment of therapeutic response, and the development of personalized treatment strategies. The processing of digital images through validated machine learning algorithms, neural networks, and related approaches is aligned with the priorities of the National Health Strategy 2030 for digitalization and the development of eHealth (Policy 2.5). The establishment of a National Digital Platform for Medical Diagnostics is envisaged, aimed at supporting all medical specialties. This platform will be integrated with the National Health Information System and the electronic patient record, with the primary objective of improving the quality of healthcare services. The integration of AI into medical practice opens new opportunities to support early diagnosis and improved management of patients with microangiopathy, facilitating timely detection and the prevention of complications, with the ultimate aim of ensuring better quality of life and preserved functional capacity.

Background Dermatology has been proven to be well suited for store-and-forward telemedicine triaging. With the reduced cost of computer power and readily available deep convolutional neural networks, using the digital images collected with store-and-forward, machine learning has made it possible to create artificial intelligence (AI) models. The AI models can analyze new digital images taken with a smartphone camera and return reliable dermatology outputs within seconds. Objective The aim of this study is to demonstrate the shift from teledermatology to dermatology AI. Methods A literature search was conducted and experience from a web-based teledermatology service was also considered. Results There has been a slow uptake of teledermatology in a clinical setting and by consumers. The development of AI dermatology models has gained momentum over the last few years. Studies have shown that AI dermatology is on par with teledermatology to deliver accurate diagnoses for the most common dermatology pathologies. Conclusions Teledermatology has still not gained mass adoption, both clinically and directly by consumers. The fast pace of AI dermatology development indicates that the technology will surpass store-and-forward teledermatology as a first point of digital consultation in dermatology. Conflicts of Interest AB is the owner of iDoc24 Inc.

Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review

Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its heterogeneous phenotype and clinical course. Artificial Intelligence (AI) and Machine Learning (ML) techniques hold promise in transforming the role of Electrocardiography (ECG) in HCM diagnosis, prognosis, and management.AI, including Deep Learning (DL), enables computers to learn patterns from data, allowing for the development of models capable of analyzing ECG signals. DL models, such as convolutional neural networks, have shown promise in accurately identifying HCM-related abnormalities in ECGs, surpassing traditional diagnostic methods.In diagnosing HCM, ML models have demonstrated high accuracy in distinguishing between HCM and other cardiac conditions, even in cases with normal ECG findings. Additionally, AI models have enhanced risk assessment by predicting arrhythmic events leading to sudden cardiac death and identifying patients at risk for atrial fibrillation and heart failure. These models incorporate clinical and imaging data, offering a comprehensive evaluation of patient risk profiles. Challenges remain, including the need for larger and more diverse datasets to improve model generalizability and address imbalances inherent in rare event prediction. Nevertheless, AI-driven approaches have the potential to revolutionize HCM management by providing timely and accurate diagnoses, prognoses, and personalized treatment strategies based on individual patient risk profiles.This review explores the current landscape of AI applications in ECG analysis for HCM, focusing on advancements in AI methodologies and their specific implementation in HCM care.

Convolutional Neural Networks (CNNs) have become the dominant paradigm in computer vision since the ImageNet breakthrough, establishing a solid engineering foundation in both cloud and edge scenarios. Addressing the current status where existing reviews often focus on a single dimension and lack an integrated engineering perspective, this paper systematically reviews the architectural evolution, key components, and application practices of modern CNNs, primarily focusing on image classification. First, the paper elucidates the design principles of key components, including convolution and receptive fields (RF) , normalization and activation functions , and attention mechanisms. It then traces the evolution path of modern hybrid backbones, from AlexNet/ResNet to MobileNet/EfficientNet, and further to ConvNeXt , following a timeline of "deepening and residualization – lightweight and automated scaling – large kernels and Transformer fusion". Second, by integrating typical application scenarios such as object detection, semantic segmentation, and super-resolution , this review distills reusable training recipes and efficiency optimization strategies involving the synergistic use of pruning, quantization, and distillation. It also provides a checklist for evaluation and deployment geared toward actual hardware. Finally, the article analyzes challenges such as long-tail categories, cross-domain distribution shift, and on-device computational constraints, and looks forward to future trends in self-supervised learning, hardware-aware design, and model robustness optimization.

Due to the complex nature of the chemical compositions of ignitable liquids (IL) and the interferences from fire debris matrices, interpreting chromatographic data poses challenges to analysts. In this work, artificial intelligence (AI) was developed by transfer learning in a convolutional neural network (CNN), GoogLeNet. The image classification AI was fine-tuned to create intelligent classification systems to discriminate samples containing gasoline residues from burned substrates. All ground truth samples were analyzed by headspace solid-phase microextraction (HS-SPME) coupled with a gas chromatograph and mass spectrometer (GC/MS). The HS-SPME-GC/MS data were transformed into three types of image presentations, that is, heatmaps, extracted ion heatmaps, and total ion chromatograms. The abundance and mass-to-charge ratios of each scan were converted into image patterns that are characteristic of the chemical profiles of gasoline. The transfer learning data were labeled as "gasoline present" and "gasoline absent" classes. The assessment results demonstrated that all AI models achieved 100 ± 0% accuracy in identifying neat gasoline. When the models were assessed using the spiked samples, the AI model developed using the extracted ion heatmap obtained the highest accuracy rate (95.9 ± 0.4%), which was greater than those obtained by other machine learning models, ranging from 17.3 ± 0.7% to 78.7 ± 0.7%. The proposed work demonstrated that the heatmaps created from GC/MS data can represent chemical features from the samples. Additionally, the pretrained CNN models are readily available in the transfer learning workflow to develop AI for GC/MS data interpretation in fire debris analysis.

High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy. A total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed. Considering all cases, the malignancy upgrade rate was 24.5%. A significant association was observed between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-underthe- curve score were obtained in classifying the data as benign or malignant. Our artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum excisions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology-pathology compatibility and by using an artificial intelligence model.

Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review

Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the "black box" nature of AI. Moreover, the fact that studies rarely use guidelines in developing and reporting AI models poses additional challenges in trusting and adapting models for practical implementation. This review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine. We will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented. The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023. Guidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks-as will be done in the proposed review-will provide comprehensive insights into current gaps and help to formulate future research directions. DERR1-10.2196/47105.