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This review addresses the disease diagnosis from brain, eye, and lung scan images based on non-invasive imaging technologies using the Internet of Medical Things (IoMT) and Artificial Intelligence (AI) systems, a topic that has been neglected in the recent literature. Combining imaging modalities with IoMT and AI is expected to enhance both medical diagnoses and personalized treatment plans. We searched various scientific databases for details on IoMT and AI in medical imaging technologies from 2019 to 2023, focusing on different imaging modalities. We investigated the performance of AI-based algorithms in imaging modalities such as X-ray, Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, and Optical Coherence Tomography using the following metrics: accuracy, precision, recall, sensitivity, specificity, and F-1 score, and then analyzed their balanced performance in six issues: enhancement of medical image quality, improvement of clinical diagnoses, support for clinical decision-making, consideration of input data, time efficiency, and data management. Advanced understanding of the IoMT and AI applications in medical imaging technologies would help identify unexplored opportunities and provide directions for future research to enhance the clinical applicability. [ABSTRACT FROM AUTHOR]

Introduction: Medical phantoms play a crucial role in medical imaging and therapy. However, the successful development of these phantoms heavily relies on a comprehensive understanding of the requirements specific to each application. Methods: In this paper, we emphasize the significance of requirement analysis in medical phantom development and develop a novel approach for gathering and classifying requirements specific for phantom development. Results: The implemented survey tool is designed to accommodate the diverse needs of stakeholders involved in phantom development, including medical staff, physicists, engineers, and product developers. To validate the effectiveness of the approach, we conduct the development of a multimodal deformable pelvic phantom, providing insights into the process and its applicability. Discussion: The results demonstrate the utility and reliability of our approach in systematically gathering, categorizing, and prioritizing requirements, thus facilitating the streamlined and efficient development of medical phantoms. [ABSTRACT FROM AUTHOR]

Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier diagnoses and treatment can save lives. Earlier diagnoses of malevolent cells in the lungs responsible for oxygenating the human body and expelling carbon dioxide due to significant procedures are critical. Even though a computed tomography (CT) scan is the best imaging approach in the healthcare sector, it is challenging for physicians to identify and interpret the tumour from CT scans. LC diagnosis in CT scan using artificial intelligence (AI) can help radiologists in earlier diagnoses, enhance performance, and decrease false negatives. Deep learning (DL) for detecting lymph node contribution on histopathological slides has become popular due to its great significance in patient diagnoses and treatment. This study introduces a computer-aided diagnosis for LC by utilizing the Waterwheel Plant Algorithm with DL (CADLC-WWPADL) approach. The primary aim of the CADLC-WWPADL approach is to classify and identify the existence of LC on CT scans. The CADLC-WWPADL method uses a lightweight MobileNet model for feature extraction. Besides, the CADLC-WWPADL method employs WWPA for the hyperparameter tuning process. Furthermore, the symmetrical autoencoder (SAE) model is utilized for classification. An investigational evaluation is performed to demonstrate the significant detection outputs of the CADLC-WWPADL technique. An extensive comparative study reported that the CADLC-WWPADL technique effectively performs with other models with a maximum accuracy of 99.05% under the benchmark CT image dataset. [ABSTRACT FROM AUTHOR]

The rapid growth of 3D printing technology has transformed different fields in medicine, for example the fabrication of medical phantoms - physical models mimicking biological tissue in medical imaging and therapy. These phantoms play a crucial role in quality assurance, education, research and training. Unlike traditional methods, 3D printing enables the creation of anatomically accurate, cost-effective, modular, and customizable phantoms with high geometric freedom. A key advantage is the ability to produce patient-based models, tailored to individual anatomies and pathologies using medical imaging data. The workflow involves processing imaging data, refining 3D models, and selecting suitable 3D printing materials. This overview focuses on the workflow, types of phantoms, and the selection of printing techniques and materials. Besides numerous opportunities, challenges include standardization, validation procedures, and ensuring reliability across different systems. The potential of 3D printing in medical phantom development is evident, promising realistic, cost-effective, and personalized solutions for improved medical research. [ABSTRACT FROM AUTHOR]

Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. However, some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. This work studies the effect of these challenges at the intra- and inter-domain level in few-shot learning scenarios with severe data imbalance. For this, we propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance. Specifically, different initialization and data augmentation methods are analyzed, and four adaptations to Siamese networks of solutions to deal with imbalanced data are introduced, including data balancing and weighted loss, both separately and combined, and with a different balance of pairing ratios. Moreover, we also assess the inference process considering four classifiers, namely Histogram, kNN, SVM, and Random Forest. Evaluation is performed on three chest X-ray datasets with annotated cases of both positive and negative COVID-19 diagnoses. The accuracy of each technique proposed for the Siamese architecture is analyzed separately. The results are compared to those obtained using equivalent methods on a state-of-the-art CNN, achieving an average F1 improvement of up to 3.6%, and up to 5.6% of F1 for intra-domain cases. We conclude that the introduced techniques offer promising improvements over the baseline in almost all cases and that the technique selection may vary depending on the amount of data available and the level of imbalance. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) and diabetic macular edema (DME) are both serious eye conditions associated with diabetes and if left untreated, and they can lead to permanent blindness. Traditional methods for screening these conditions rely on manual image analysis by experts, which can be time-consuming and costly due to the scarcity of such experts. To overcome the aforementioned challenges, we present the Modified CornerNet approach with DenseNet-100. This system aims to localize and classify lesions associated with DR and DME. To train our model, we first generate annotations for input samples. These annotations likely include information about the location and type of lesions within the retinal images. DenseNet-100 is a deep CNN used for feature extraction, and CornerNet is a one-stage object detection model. CornerNet is known for its ability to accurately localize small objects, which makes it suitable for detecting lesions in retinal images. We assessed our technique on two challenging datasets, EyePACS and IDRiD. These datasets contain a diverse range of retinal images, which is important to estimate the performance of our model. Further, the proposed model is also tested in the cross-corpus scenario on two challenging datasets named APTOS-2019 and Diaretdb1 to assess the generalizability of our system. According to the accomplished analysis, our method outperformed the latest approaches in terms of both qualitative and quantitative results. The ability to effectively localize small abnormalities and handle over-fitted challenges is highlighted as a key strength of the suggested framework which can assist the practitioners in the timely recognition of such eye ailments. [ABSTRACT FROM AUTHOR]

The importance of health literacy in medical imaging is well recognized, yet the current landscape remains inadequately understood. This study aims to explore the extent of health literacy studies contextualized to medical imaging. Scoping review. A scoping review was conducted using three online bibliographic databases namely, PubMed, ScienceDirect, and CINAHL. We have adopted the concept of health literacy, as a clinical risk and personal asset, to guide this review. Of 311 unique articles, 39 met our selection criteria. Five themes (categories) were identified by the authors: appropriate communication with patients who receive medical imaging test results, appropriate usage of medical imaging, classes and characteristics of eHealth literacy, disease/deterioration prevention, and patient education. Additionally, 17 health literacy assessment tools were identified, including 11 original creations. Finally, 11 recommendations have emerged from this scoping review, offering valuable insights into methods, considerations, and strategies for promoting health literacy. Health literacy studies in medical imaging cover both clinical and public health perspectives, benefiting diverse populations, regardless of underlying medical conditions. Notably, the majority of assessment tools used in these studies were author-generated, hindering cross-study comparisons. Given the innate capacity of medical images to convey intuitive information, those images do not solely benefit the patients who are given medical imaging examinations, but they also hold significant potential to enhance public health literacy. Health literacy and medical imaging are closely associated and mutually reinforce each other. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusions Radiography students complete professional placements in various clinical settings and must adhere to distinct infection prevention and control (IPC) protocols. The aim of this study was to explore radiography students' training, knowledge, attitudes, and practice (KAP) relating to IPC in the use of contrast media injectors in computed tomography (CT).An online survey study was undertaken with radiography students enrolled at two Australian universities. Survey questions related to contrast media training and KAP regarding IPC in CT. Data was summarised using descriptive statistics, with comparisons between experience in public and private practice. One free‐text response question focused on non‐adherence to IPC best practice, analysed using content analysis.In total, 40 students completed the survey (9% response rate). Reports of IPC and contrast media equipment training was high, with disposition for further training. Regarding IPC knowledge, 65% of students responded correctly to all ‘knowledge’ items (individual scores range: 60–100%). Low consensus was observed regarding whether gloves replace the need for hand hygiene and if CT contrast tubing poses risk to healthcare workers (85% each). Mean scores ranged from 41% to 100% regarding identification of sterile syringe and tubing components. Responses to the open‐ended question were categorised into four themes: ‘High non‐adherence risk working conditions’, ‘attitudes and practice’, ‘knowledge’, and ‘prioritise good IPC practice’.Radiography students demonstrate varied comprehension of IPC regarding contrast media equipment, and results suggest need for collaborative efforts between academic institutions and clinical training sites to integrate IPC protocols into curricula and on‐site training. [ABSTRACT FROM AUTHOR]

This study aims to illuminate the enduring contributions of underrepresented pioneers in radiology, emphasizing their resilience, innovations, and the significant barriers they overcame. By weaving their achievements into the broader narrative of medical science, this research highlights the critical role of diversity and progress in the evolution of radiology. This narrative review chronicles the significant contributions of underrepresented radiologists from the early 20th century to the present. By synthesizing historical data, biographical sketches, and contemporary medical literature, we highlight the pivotal roles these pioneers have played in advancing radiology. Their groundbreaking work not only enhanced medical imaging technologies and practices but also championed the cause of diversity and inclusion within the field. These stories of perseverance and innovation underscore the ongoing need for an inclusive approach in the medical community, reflecting on how diversity has shaped and will continue to influence the evolution of radiology. The study identifies several pivotal figures, such as Marcus F. Wheatland, the first known African American radiologist, and Ivy O. Roach Brooks, the first woman to lead a radiology department at a major U.S. hospital. It explores their wide-ranging contributions from clinical practice and education to leadership and advocacy for diversity within the medical profession. The legacies of these radiologists illuminate not just their individual accomplishments but also reflect the broader struggle for equality and representation in the medical field. Their determination and excellence have paved the way for future generations, significantly enhancing the inclusivity and diversity of the radiology field. Understanding the contributions of these underrepresented radiologists enriches the field's perspective on diversity, equity, and inclusion. Highlighting these pioneers underscores the importance of mentorship, representation, and advocacy in creating an environment where all talented individuals can thrive. Insights from this historical analysis are crucial for shaping future policies and practices in radiology and medical education, ensuring the continuation of these trailblazers' inspiring legacy. [ABSTRACT FROM AUTHOR]

Rapid changes in artificial intelligence (AI) have already impacted the medical field. While the use of AI to assist medical diagnosis has been documented, AI is continually expanding within medical applications. AI applications in sonography and their effect on ultrasound examinations and sonographers are still indeterminate. Six papers were reviewed to investigate AI applications and effects within the sonography field. These papers provided results on a range of ultrasound applications including breast, obstetric, skin lesions, carotid, blood flow and cardiac ultrasound imaging when combined with AI. In this narrative review, the application of AI demonstrated that accuracy and speed of clinical diagnosis can be improved. These six aspects of ultrasound imaging combined with AI demonstrated the potential to assist the operator and clinicians with a diagnosis in various applications and settings. Additionally, AI can be beneficial to telehealth applications for rural and remote areas where healthcare access can be limited. These changes are opportunities to assist with medical care to provide benefits to patients, sonographers and clinicians as AI transitions to a positive integration within many aspects of clinical care. [ABSTRACT FROM AUTHOR]

In medical imaging, traditional methods have long been relied upon. However, the integration of Generative Adversarial Networks (GANs) has sparked a paradigm shift, ushering in a new era of innovation. Our comprehensive investigation explores the groundbreaking impact of GANs on medical imaging, examining the evolution from traditional techniques to GAN-driven approaches. Through meticulous analysis, we dissect various aspects of GANs, encompassing their taxonomy, historical progression, and diverse iterations such as Self-Attention GANs (SAGAN), Conditional GANs, and Progressive Growing GANs (PGGAN). Complemented by a practical case study, we scrutinize the extensive applications of GANs, spanning image generation, reconstruction, enhancement, segmentation, and super-resolution. Despite promising prospects, enduring challenges including data scarcity, interpretability issues, and ethical concerns persist. Looking ahead, we anticipate advancements in personalized and pathological image generation, cross-modal synthesis, real-time interactive image generation, and enhanced anomaly detection. Through this review, we underscore the transformative potential of GANs in reshaping medical imaging practices, while also outlining avenues for future research endeavors. [ABSTRACT FROM AUTHOR]

The global health crisis of COVID-19 has ushered in an era of unprecedented data generation, encompassing the virus's transmission patterns, societal consequences, and governmental responses. Data mining has emerged as a pivotal tool for extracting invaluable insights from this voluminous dataset, offering critical support for informed decision-making. While existing surveys primarily explore methodologies for detecting COVID-19 in medical imagery and official sources, this article comprehensively examines the pandemic through big data mining. We emphasize the significance of social network analysis, shedding light on the pandemic's profound influence on community socio-economic behavior. Additionally, we illuminate advancements in diverse domains, encompassing behavioral impact analysis on social media, contact tracing implications, early disease screening through medical imaging, and insights derived from health-related time-series data analytics. Our study further organizes the literature by categorizing it based on data sources, dataset types, analytical approaches, techniques, and application scenarios. Finally, we delineate prevailing challenges and forthcoming research prospects, charting the course for future investigations. [ABSTRACT FROM AUTHOR]

Multiple pathologic conditions can lead to a diseased and symptomatic glenohumeral joint for which total shoulder arthroplasty (TSA) replacement may be indicated. The long-term survival of implants is limited. With the increasing incidence of joint replacement surgery, it can be anticipated that joint replacement revision surgery will become more common. It can be challenging at times to retrieve the manufacturer of the in situ implant. Therefore, certain systems facilitated by AI techniques such as deep learning (DL) can help correctly identify the implanted prosthesis. Correct identification of implants in revision surgery can help reduce perioperative complications and complications. DL was used in this study to categorise different implants based on X-ray images into four classes (as a first case study of the small dataset): Cofield, Depuy, Tornier, and Zimmer. Imbalanced and small public datasets for shoulder implants can lead to poor performance of DL model training. Most of the methods in the literature have adopted the idea of transfer learning (TL) from ImageNet models. This type of TL has been proven ineffective due to some concerns regarding the contrast between features learnt from natural images (ImageNet: colour images) and shoulder implants in X-ray images (greyscale images). To address that, a new TL approach (self-supervised pertaining (SSP)) is proposed to resolve the issue of small datasets. The SSP approach is based on training the DL models (ImageNet models) on a large number of unlabelled greyscale medical images in the domain to update the features. The models are then trained on a small labelled data set of X-ray images of shoulder implants. The SSP shows excellent results in five ImageNet models, including MobilNetV2, DarkNet19, Xception, InceptionResNetV2, and EfficientNet with precision of 96.69%, 95.45%, 98.76%, 98.35%, and 96.6%, respectively. Furthermore, it has been shown that different domains of TL (such as ImageNet) do not significantly affect the performance of shoulder implants in X-ray images. A lightweight model trained from scratch achieves 96.6% accuracy, which is similar to using standard ImageNet models. The features extracted by the DL models are used to train several ML classifiers that show outstanding performance by obtaining an accuracy of 99.20% with Xception+SVM. Finally, extended experimentation has been carried out to elucidate our approach’s real effectiveness in dealing with different medical imaging scenarios. Specifically, five different datasets are trained and tested with and without the proposed SSP, including the shoulder X-ray with an accuracy of 99.47% and CT brain stroke with an accuracy of 98.60%. [ABSTRACT FROM AUTHOR]

Traditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL. [ABSTRACT FROM AUTHOR]

Objective: Obtaining automated, objective 3‐dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies and interventions. We aim to develop a deep learning (DL) pipeline to automatically segment the ET and ICA and use these segmentations to compute distances between these structures. Study Design: Retrospective cohort. Setting: Tertiary referral center. Methods: From a database of 30 CT scans, 60 ET and ICA pairs were manually segmented and used to train an nnU‐Net model, a DL segmentation framework. These segmentations were also used to develop a quantitative tool to capture the magnitude and location of the minimum distance point (MDP) between ET and ICA. Performance metrics for the nnU‐Net automated segmentations were calculated via the average Hausdorff distance (AHD) and dice similarity coefficient (DSC). Results: The AHD for the ET and ICA were 0.922 and 0.246 mm, respectively. Similarly, the DSC values for the ET and ICA were 0.578 and 0.884. The mean MDP from ET to ICA in the cartilaginous region was 2.6 mm (0.7‐5.3 mm) and was located on average 1.9 mm caudal from the bony cartilaginous junction. Conclusion: This study describes the first end‐to‐end DL pipeline for automated ET and ICA segmentation and analyzes distances between these structures. In addition to helping to ensure the safe selection of patients for ET dilation, this method can facilitate large‐scale studies exploring the relationship between ET pathologies and the 3D shape of the ET. [ABSTRACT FROM AUTHOR]

Diagnosis and treatment of urological tumors, relying on auxiliary data such as medical imaging, while incorporating individual patient characteristics into treatment selection, has long been a key challenge in clinical medicine. Traditionally, clinicians used extensive experience for decision-making, but recent artificial intelligence (AI) advancements offer new solutions. Machine learning (ML) and deep learning (DL), notably convolutional neural networks (CNNs) in medical image recognition, enable precise tumor diagnosis and treatment. These technologies analyze complex medical image patterns, improving accuracy and efficiency. AI systems, by learning from vast datasets, reveal hidden features, offering reliable diagnostics and personalized treatment plans. Early detection is crucial for tumors like renal cell carcinoma (RCC), bladder cancer (BC), and Prostate Cancer (PCa). AI, coupled with data analysis, improves early detection and reduces misdiagnosis rates, enhancing treatment precision. AI's application in urological tumors is a research focus, promising a vital role in urological surgery with improved patient outcomes. This paper examines ML, DL in urological tumors, and AI's role in clinical decisions, providing insights for future AI applications in urological surgery. [ABSTRACT FROM AUTHOR]

Skin cancer is a dreadful disease, which is mainly caused due to the heavy exposure of the human body to the ultraviolet rays emitted from the sun. Although the mortality rate is very high, the survival rate is found to be superior when it is detected at its early stage. In this research, Chronological Dingo Optimizer (CDO)-based Deep Maxout Network (DMN) is developed for the skin cancer detection. Here, initially the images are pre-processed and then, the skin lesion segmentation is accomplished effectively by employing Double U-Net. Then, data augmentation is performed and the detection process is carried out by employing DMN, where the network is optimally fine-tuned utilizing designed CDO. The CDO is the integration of chronological concept with Dingo Optimizer (DOX). The proposed scheme shows better outcomes with superior sensitivity of 0.959, F-measure of 0.908, accuracy of 0.923 and specificity of 0.837. [ABSTRACT FROM AUTHOR]

Fluorescence guidance is routinely used in surgery to enhance perfusion contrast in multiple types of diseases. Pressure-enhanced sensing of tissue oxygenation (PRESTO) via fluorescence is a technique extensively analyzed here, that uses an FDA-approved human precursor molecule, 5-aminolevulinic acid (ALA), to stimulate a unique delayed fluorescence signal that is representative of tissue hypoxia. The ALA precontrast agent is metabolized in most tissues into a red fluorescent molecule, protoporphyrin IX (PpIX), which has both prompt fluorescence, indicative of the concentration, and a delayed fluorescence, that is amplified in low tissue oxygen situations. Applied pressure from palpation induces transient capillary stasis and a resulting transient PRESTO contrast, dominant when there is near hypoxia. This study examined the kinetics and behavior of this effect in both normal and tumor tissues, with a prolonged high PRESTO contrast (contrast to background of 7.3) across 5 tumor models, due to sluggish capillaries and inhibited vasodynamics. This tissue function imaging approach is a fundamentally unique tool for real-time palpation-induced tissue response in vivo, relevant for chronic hypoxia, such as vascular diseases or oncologic surgery. [ABSTRACT FROM AUTHOR]

Pneumonia, a significant contributor to global disease and mortality, imposes a substantial burden on healthcare systems. Conventional approaches to pneumonia detection rely on constrained datasets and traditional feature extraction techniques, potentially resulting in suboptimal accuracy. To overcome these limitations, we introduce the "SyntDiaNet" framework, an innovative solution that amalgamates synthetic data generation and advanced diagnostic capabilities. This research presents a comprehensive methodology aimed at enhancing pneumonia detection precision. The central innovation revolves around the integration of a Classifier-Embedded Generative Adversarial Network (CE-GAN) for data augmentation, followed by a multifaceted feature extraction process. This process encompasses Contour Detection, Gabor Texture Analysis, and Blob Detection, to capture nuanced patterns that may indicate pneumonia in X-ray images. Leveraging broad knowledge, an unsupervised feature extraction step is integrated using the SyntDiaNet Transfer Learning model. Across diverse feature extraction strategies, combined with the CE-GAN dataset and SyntDiaNet model, accuracy rates demonstrate substantial improvements, with potential ranges of 90.5% to 97.6%. The precision ranged between 0.932 and 0.981; recall scores spanned from 0.896 to 0.987; F1 scores varied between 0.90 and 0.980, and the AUC-ROC values ranged from 0.90 to 0.97, showcasing the ability of the models to discriminate between positive and negative classes, emphasizing differences in the classifiers' overall performance in ranking instances. By utilizing synthetic data and advanced feature extraction techniques, it adeptly addresses challenges related to limited datasets and imbalanced class distributions. Furthermore, its adaptability to various medical imaging tasks underscores its potential impact across a range of healthcare applications. [ABSTRACT FROM AUTHOR]

Meeting the rising global demand for healthcare diagnostic tools is crucial, especially with a shortage of medical professionals. This issue has increased interest in utilizing deep learning (DL) and telemedicine technologies. DL, a branch of artificial intelligence, has progressed due to advancements in digital technology and data availability and has proven to be effective in solving previously challenging learning problems. Convolutional neural networks (CNNs) show potential in image detection and recognition, particularly in healthcare applications. However, due to their resource-intensiveness, they surpass the capabilities of general-purpose CPUs. Therefore, hardware accelerators such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been developed. With their parallelism efficiency and energy-saving capabilities, FPGAs have gained popularity for DL networks. This research aims to automate the classification of normal and abnormal (specifically Diabetic Foot Ulcer—DFU) classes using various parallel hardware accelerators. The study introduces two CNN models, namely DFU_FNet and DFU_TFNet. DFU_FNet is a simple model that extracts features used to train classifiers like SVM and KNN. On the other hand, DFU_TFNet is a deeper model that employs transfer learning to test hardware efficiency on both shallow and deep models. DFU_TFNet has outperformed AlexNet, VGG16, and GoogleNet benchmarks with an accuracy 99.81%, precision 99.38% and F1-Score 99.25%. In addition, the study evaluated two high-performance computing platforms, GPUs and FPGAs, for real-time system requirements. The comparison of processing time and power consumption revealed that while GPUs outpace FPGAs in processing speed, FPGAs exhibit significantly lower power consumption than GPUs. [ABSTRACT FROM AUTHOR]

Adhesive Capsulitis of the shoulder is a painful pathology limiting shoulder movements, commonly known as "Frozen Shoulder". Since this pathology limits movement, it is important to make an early diagnosis. Diagnosing capsulitis relies on clinical assessment, although diagnostic imaging, such as Magnetic Resonance Imaging, can provide predictive or supportive information for specific characteristic signs. However, its diagnosis is not so simple nor so immediate, indeed it remains a difficult topic for many general radiologists and expert musculoskeletal radiologists. This study aims to investigate whether it is possible to use disease signs within a medical image to automatically diagnose Adhesive Capsulitis. To this purpose, we propose an automatic Model Checking-based approach to quickly diagnose the Adhesive Capsulitis taking as input the radiomic feature values from the medical images. Furthermore, we compare the performance achieved by our method with diagnostic results obtained by professional radiologists with different levels of experience. To the best of our knowledge, this is the first method for the automatic diagnosis of Adhesive Capsulitis of the Shoulder. [ABSTRACT FROM AUTHOR]

The rapid development of deep learning in medical imaging has significantly enhanced the capabilities of artificial intelligence while simultaneously introducing challenges, including the need for vast amounts of training data and the labor-intensive tasks of labeling and segmentation. Generative adversarial networks (GANs) have emerged as a solution, offering synthetic image generation for data augmentation and streamlining medical image processing tasks through models such as cGAN, CycleGAN, and StyleGAN. These innovations not only improve the efficiency of image augmentation, reconstruction, and segmentation, but also pave the way for unsupervised anomaly detection, markedly reducing the reliance on labeled datasets. Our investigation into GANs in medical imaging addresses their varied architectures, the considerations for selecting appropriate GAN models, and the nuances of model training and performance evaluation. This paper aims to provide radiologists who are new to GAN technology with a thorough understanding, guiding them through the practical application and evaluation of GANs in brain imaging with two illustrative examples using CycleGAN and pixel2style2pixel (pSp)-combined StyleGAN. It offers a comprehensive exploration of the transformative potential of GANs in medical imaging research. Ultimately, this paper strives to equip radiologists with the knowledge to effectively utilize GANs, encouraging further research and application within the field. [ABSTRACT FROM AUTHOR]

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network's determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region of interest (ROI) cropping, and data augmentation steps to enhance classification accuracy. Later, a multi-layer Gradient CAM (ML-Grad-CAM) algorithm is integrated to generate a class-specific saliency map for improved visualization in CXR images. We also define and calculate a Severity Assessment Index (SAI) from the saliency map to quantitatively measure infection severity. The trained model achieved an accuracy score of 96.44% for the three-class CXR classification task, i.e., COVID-19, pneumonia, and normal (healthy patients), outperforming many existing techniques in the literature. The saliency maps generated from the proposed ML-GRAD-CAM algorithm are compared with the original Gran-CAM algorithm. [ABSTRACT FROM AUTHOR]

Medical imaging is essential for pathology diagnosis and treatment, enhancing decision making and reducing costs, but despite various computational methodologies proposed to improve imaging modalities, further optimization is needed for broader acceptance. This study explores deep learning (DL) methodologies for classifying and segmenting pathological imaging data, optimizing models to accurately predict and generalize from training to new data. Different CNN and U-Net architectures are implemented for segmentation tasks, with their performance evaluated on histological image datasets using enhanced pre-processing techniques such as resizing, normalization, and data augmentation. These are trained, parameterized, and optimized using metrics such as accuracy, the DICE coefficient, and intersection over union (IoU). The experimental results show that the proposed method improves the efficiency of cell segmentation compared to networks, such as U-NET and W-UNET. The results show that the proposed pre-processing has improved the IoU from 0.9077 to 0.9675, about 7% better results; also, the values of the DICE coefficient obtained improved from 0.9215 to 0.9916, about 7% better results, surpassing the results reported in the literature. [ABSTRACT FROM AUTHOR]

Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various tasks and attracted increasing interest as a natural language interface across many domains. Recently, large vision-language models (VLMs) that learn rich vision–language correlation from image–text pairs, like BLIP-2 and GPT-4, have been intensively investigated. However, despite these developments, the application of LLMs and VLMs in image quality assessment (IQA), particularly in medical imaging, remains unexplored. This is valuable for objective performance evaluation and potential supplement or even replacement of radiologists' opinions. To this end, this study introduces IQAGPT, an innovative computed tomography (CT) IQA system that integrates image-quality captioning VLM with ChatGPT to generate quality scores and textual reports. First, a CT-IQA dataset comprising 1,000 CT slices with diverse quality levels is professionally annotated and compiled for training and evaluation. To better leverage the capabilities of LLMs, the annotated quality scores are converted into semantically rich text descriptions using a prompt template. Second, the image-quality captioning VLM is fine-tuned on the CT-IQA dataset to generate quality descriptions. The captioning model fuses image and text features through cross-modal attention. Third, based on the quality descriptions, users verbally request ChatGPT to rate image-quality scores or produce radiological quality reports. Results demonstrate the feasibility of assessing image quality using LLMs. The proposed IQAGPT outperformed GPT-4 and CLIP-IQA, as well as multitask classification and regression models that solely rely on images. [ABSTRACT FROM AUTHOR]

Introduction: Statistical shape modelling (SSM) is used to analyse morphology, discover qualitatively and quantitatively unique shape features within a population, and generate mean shapes and shape modes that show morphological variability. Hierarchical agglomerative clustering is a machine learning analysis used to identify subgroups within a given population in relation to shape features. We tested the application of both methods in the clinically relevant scenario of patients undergoing aortic valve repair (AVR). Every year, around 5000 patients undergo surgical AVR in the UK. Aims: Evaluate aortic morphology and identify subgroups amongst patients who had undergone AVR, including Ozaki, Ross, and valve-sparing procedures using SSM and unsupervised hierarchical clustering analysis. This methodological framework can evaluate both pre- and post-surgical variability across subgroups undergoing different surgeries. Methods: Pre- (n = 47) and post- (n = 35) operative three-dimensional (3D) aortic models were reconstructed from computed tomography (CT) and cardiac magnetic resonance (CMR) images. Computational analyses for SSM and hierarchical clustering were run separately for the two subgroups, assessing (a) ascending aorta only and (b) the whole aorta. This allows for exploring possible variations in morphological classification related to the input shape. Results: Most patients in the Ross procedure subgroup exhibited differences in aortic morphology from other subgroups, including an elongated ascending and wide aortic arch pre-operatively, and an elongated ascending aorta with a slightly enlarged sinus post-operatively. In hierarchical clustering, the Ross aortas also appeared to cluster together compared to the other surgical procedures, both pre-operatively and post-operatively. There were significant differences between clusters in terms of clustering distance in the pre-operative analyses (p = 0.003 for ascending aortas, p = 0.016 for whole aortas). There were no significant differences between the clusters in post-operative analyses (p = 0.47 for ascending, p = 0.19 for whole aorta). Conclusions: We demonstrated the feasibility of evaluating aortic morphology before and after different aortic valve surgeries using SSM and hierarchical clustering. This framework could be used to further explore shape features associated with surgical decision-making pre-operatively and, importantly, to identify subgroups whose morphology is associated with poorer clinical outcomes post-operatively. Statistical shape modelling (SSM) and unsupervised hierarchical clustering are two statistical methods that can be used to assess morphology, show morphological variations, with the latter being able to identify subgroups within a population. These methods have been applied to the population of aortic valve replacement (AVR) patients since there are different surgical procedures (traditional AVR, Ozaki, Ross, and valve-sparing). The aim is to evaluate aortic morphology and identify subgroups within this population before and after surgery. Computed tomography and cardiac magnetic resonance images were reconstructed into 3D models of the ascending aorta and whole aorta, which were then input into SSM and hierarchical clustering. The results show that the Ross aortic morphology is quite different from the other aortas. The clustering did not classify the aortas based on the surgical procedures; however, most of the Ross group did cluster together, indicating low variability within this surgical group. [ABSTRACT FROM AUTHOR]

The aim of this study was to experimentally assess the luminescence efficiency of a cerium fluoride (CeF3) inorganic scintillator in crystal form as a possible alternative to high-luminescence but hygroscopic cerium bromide (CeBr3). The experiments were performed under typical diagnostic radiology X-rays (50–140 kVp). Parameters such as the crystal's absolute luminescence efficiency (AE) and the spectral matching with a series of optical detectors were examined. The replacement of bromine with fluorine appeared to drastically reduce the AE of CeF3 compared to CeBr3 and other commercially available inorganic scintillators such as bismuth germanate (Bi4Ge3O12-BGO). CeF3 reaches a maximum luminescence efficiency value of only 0.8334 efficiency units (EUs) at 140 kVp, whereas the corresponding values for CeBr3 and BGO were 29.49 and 3.41, respectively. Furthermore, the emission maximum (at around 313 nm) moved towards the lower part of the visible spectrum, making CeF3 suitable for spectral coupling with various photocathodes and photomultipliers applied in nuclear medicine detectors, but completely unsuitable for spectral matching with CCDs and CMOS. The obtained luminescence efficiency results denote that CeF3 cannot be applied in medical imaging applications covering the range 50–140 kVp; however, examination of its luminescence output in the nuclear medicine energy range (~70 to 511 keV) could reveal possible applicability in these modalities. [ABSTRACT FROM AUTHOR]

Cervical cancer is caused in the vast majority of cases by the human papilloma virus (HPV) through sexual contact and requires a specific molecular-based analysis to be detected. As an HPV vaccine is available, the incidence of cervical cancer is up to ten times higher in areas without adequate healthcare resources. In recent years, liquid cytology has been used to overcome these shortcomings and perform mass screening. In addition, classifiers based on convolutional neural networks can be developed to help pathologists diagnose the disease. However, these systems always require the final verification of a pathologist to make a final diagnosis. For this reason, explainable AI techniques are required to highlight the most significant data to the healthcare professional, as it can be used to determine the confidence in the results and the areas of the image used for classification (allowing the professional to point out the areas he/she thinks are most important and cross-check them against those detected by the system in order to create incremental learning systems). In this work, a 4-phase optimization process is used to obtain a custom deep-learning classifier for distinguishing between 4 severity classes of cervical cancer with liquid-cytology images. The final classifier obtains an accuracy over 97% for 4 classes and 100% for 2 classes with execution times under 1 s (including the final report generation). Compared to previous works, the proposed classifier obtains better accuracy results with a lower computational cost. [ABSTRACT FROM AUTHOR]

Our research evaluates advanced artificial (AI) methodologies to enhance diagnostic accuracy in pulmonary radiography. Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model's performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings. [ABSTRACT FROM AUTHOR]

In this work, several machine learning (ML) algorithms, both classical ML and modern deep learning, were investigated for their ability to improve the performance of a pipeline for the segmentation and classification of prostate lesions using MRI data. The algorithms were used to perform a binary classification of benign and malignant tissue visible in MRI sequences. The model choices include support vector machines (SVMs), random decision forests (RDFs), and multi-layer perceptrons (MLPs), along with radiomic features that are reduced by applying PCA or mRMR feature selection. Modern CNN-based architectures, such as ConvNeXt, ConvNet, and ResNet, were also evaluated in various setups, including transfer learning. To optimize the performance, different approaches were compared and applied to whole images, as well as gland, peripheral zone (PZ), and lesion segmentations. The contribution of this study is an investigation of several ML approaches regarding their performance in prostate cancer (PCa) diagnosis algorithms. This work delivers insights into the applicability of different approaches for this context based on an exhaustive examination. The outcome is a recommendation or preference for which machine learning model or family of models is best suited to optimize an existing pipeline when the model is applied as an upstream filter. [ABSTRACT FROM AUTHOR]

Supervised learning has been the cornerstone of many successful medical imaging applications. However, its reliance on large labeled datasets poses significant challenges, especially in the medical domain, where data annotation is time-consuming and expensive. In response, self-supervised learning (SSL) has emerged as a promising alternative, leveraging unlabeled data to learn meaningful representations without explicit supervision. This paper provides a detailed overview of supervised learning and its limitations in medical imaging, underscoring the need for more efficient and scalable approaches. The study emphasizes the importance of the area under the curve (AUC) as a key evaluation metric in assessing SSL performance. The AUC offers a comprehensive measure of model performance across different operating points, which is crucial in medical applications, where false positives and negatives have significant consequences. Evaluating SSL methods based on the AUC allows for robust comparisons and ensures that models generalize well to real-world scenarios. This paper reviews recent advances in SSL for medical imaging, demonstrating their potential to revolutionize the field by mitigating challenges associated with supervised learning. Key results show that SSL techniques, by leveraging unlabeled data and optimizing performance metrics like the AUC, can significantly improve the diagnostic accuracy, scalability, and efficiency in medical image analysis. The findings highlight SSL's capability to reduce the dependency on labeled datasets and present a path forward for more scalable and effective medical imaging solutions. [ABSTRACT FROM AUTHOR]

In this work, we propose to tackle the problem of domain generalization in the context of insufficient samples. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specifically, we first extend empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions rather than latent points. Moreover, instead of imposing the contrastive semantic alignment (CSA) loss based on pairs of latent points, a novel probabilistic CSA loss encourages positive probabilistic embedding pairs to be closer while pulling other negative ones apart. Benefiting from the learned representation captured by probabilistic models, our proposed method can marriage the measurement on the distribution over distributions (i.e., the global perspective alignment) and the distribution-based contrastive semantic alignment (i.e., the local perspective alignment). Extensive experimental results on three challenging medical datasets show the effectiveness of our proposed method in the context of insufficient data compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Radiography images inherently possess globally consistent structures while exhibiting significant diversity in local anatomical regions, making it challenging to model their normal features through unsupervised anomaly detection. Since unsupervised anomaly detection methods localize anomalies by utilizing discrepancies between learned normal features and input abnormal features, previous studies introduce a memory structure to capture the normal features of radiography images. However, these approaches store extremely localized image segments in their memory, causing the model to represent both normal and pathological features with the stored components. This poses a significant challenge in unsupervised anomaly detection by reducing the disparity between learned features and abnormal features. Furthermore, with the diverse settings in radiography imaging, the above issue is exacerbated: more diversity in the normal images results in stronger representation of pathological features. To resolve the issues above, we propose a novel pathology detection method called Patch-wise Vector Quantization (P-VQ). Unlike the previous methods, P-VQ learns vector-quantized representations of normal "patches" while preserving its spatial information by incorporating vector similarity metric. Furthermore, we introduce a novel method for selecting features in the memory to further enhance the robustness against diverse imaging settings. P-VQ even mitigates the "index collapse" problem of vector quantization by proposing top- k % dropout. Our extensive experiments on the BMAD benchmark demonstrate the superior performance of P-VQ against existing state-of-the-art methods. • Diverse radiographic imaging conditions challenge unsupervised pathology detection. • Learning normal features is essential under complex training data distribution. • We improve the existing vector quantization to learn normal features effectively. • We resolve the "codebook collapse" issue in the existing vector quantization. • We achieve state-of-the-art performance in brain MRI and retinal CT datasets. [ABSTRACT FROM AUTHOR]

In this study, we introduce an innovative approach to significantly enhance the precision and interpretability of brain tumor detection and segmentation. Our method ingeniously integrates the cutting-edge capabilities of the ChatGPT chatbot interface with a state-of-the-art multi-modal convolutional neural network (CNN). Tested rigorously on the BraTS dataset, our method showcases unprecedented performance, outperforming existing techniques in terms of both accuracy and efficiency, with an impressive Dice score of 0.89 for tumor segmentation. By seamlessly integrating ChatGPT, our model unveils deep-seated insights into the intricate decision-making processes, providing researchers and physicians with invaluable understanding and confidence in the results. This groundbreaking fusion holds immense promise, poised to revolutionize the landscape of medical imaging, with far-reaching implications for clinical practice and research. Our study exemplifies the transformative potential achieved through the synergistic combination of multi-modal CNNs and natural language processing, paving the way for remarkable advancements in brain tumor detection and segmentation. [ABSTRACT FROM AUTHOR]

The growing cancer burden in Africa demands urgent action. Medical imaging is crucial for cancer diagnosis and management and is an essential enabler of precision medicine. To understand the readiness for quantitative imaging analysis to support cancer management in Africa, we analyzed the utilization patterns of imaging modalities for cancer research across the continent. We retrieved articles by systematically searching PubMed, using a combination of search terms {"Neoplasm"} AND {"Radiology" or "Diagnostic imaging" or "Radiography" or "Interventional Radiology" or "Radiotherapy" or "Radiation Oncology"} AND {Africa∗ or 54 African countries}. Articles describing cancer diagnosis or management in humans with the utilization of imaging were included. Exclusion criteria were review articles, non-English articles, publications before 2000, noncancer diagnoses, and studies conducted outside Africa. The analysis of diagnostic imaging in Africa revealed a diverse utilization pattern across different cancer types and regions. The literature search identified 107 publications on cancer imaging in Africa. The studies were carried out in 19 African countries on 12 different cancer types with 6 imaging modalities identified. Most cancer imaging research studies used multiple imaging modalities. Ultrasound was the most used distinct imaging modality and MRI was the least frequently used. Most research studies originated from Nigeria, South Africa, and Egypt. We demonstrate substantial variability in the presence of imaging modalities, widespread utilization of ultrasonography, and limited availability of advanced imaging modalities for cancer research. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) are a form of deep learning architecture based on the zero-sum game theory, which uses real data to generate realistic fake data. GANs use two opposing neural networks working: a generator and a discriminator. They represent a powerful tool for generating realistic synthetic patient data sets and can potentially revolutionize research. This systematic literature review evaluated the scale and scope of GANs within plastic surgery, constructing a framework for its use and evaluation within subspecialties. Following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, a systematic review was performed for applications of GANs in plastic surgery from 2014 to 2022. Three independent reviewers screened from databases: PubMed, Embase, PsychInfo, Scopus, and Google Scholar. A total of 70 studies were captured by the search, of which seven studies met our criteria. The most common subspecialty was craniofacial (n = 4). Proposed uses of GANs included facial recognition, burn estimation, scar prediction, and post-breast cancer reconstruction anomaly scoring. GANs were conditional, trained on data sets averaging 54,652 ± 112,180 samples, with some sourced publicly and others being primary. GANs hold promise for advancing plastic surgery, backed by diverse applications in the literature. Studies should follow a standardized reporting structure for consistency and transparency, as outlined, especially regarding the data sets used to ensure appropriate representation from an ethnic and cultural diversity perspective. Although GANs require specialist computational expertise to create, surgeons need to understand their development by leveraging the full potential of GANs within the emerging field of computational plastic surgery and beyond. [ABSTRACT FROM AUTHOR]

Breast cancer is deadly cancer causing a considerable number of fatalities among women in worldwide. To enhance patient outcomes as well as survival rates, early and accurate detection is crucial. Machine learning techniques, particularly deep learning, have demonstrated impressive success in various image recognition tasks, including breast cancer classification. However, the reliance on large labeled datasets poses challenges in the medical domain due to privacy issues and data silos. This study proposes a novel transfer learning approach integrated into a federated learning framework to solve the limitations of limited labeled data and data privacy in collaborative healthcare settings. For breast cancer classification, the mammography and MRO images were gathered from three different medical centers. Federated learning, an emerging privacy-preserving paradigm, empowers multiple medical institutions to jointly train the global model while maintaining data decentralization. Our proposed methodology capitalizes on the power of pre-trained ResNet, a deep neural network architecture, as a feature extractor. By fine-tuning the higher layers of ResNet using breast cancer datasets from diverse medical centers, we enable the model to learn specialized features relevant to different domains while leveraging the comprehensive image representations acquired from large-scale datasets like ImageNet. To overcome domain shift challenges caused by variations in data distributions across medical centers, we introduce domain adversarial training. The model learns to minimize the domain discrepancy while maximizing classification accuracy, facilitating the acquisition of domain-invariant features. We conducted extensive experiments on diverse breast cancer datasets obtained from multiple medical centers. Comparative analysis was performed to evaluate the proposed approach against traditional standalone training and federated learning without domain adaptation. When compared with traditional models, our proposed model showed a classification accuracy of 98.8% and a computational time of 12.22 s. The results showcase promising enhancements in classification accuracy and model generalization, underscoring the potential of our method in improving breast cancer classification performance while upholding data privacy in a federated healthcare environment. [ABSTRACT FROM AUTHOR]

The usage of virtual reality (VR) in healthcare field has been gaining attention lately. The main use cases revolve around medical imaging and clinical skill training. Healthcare professionals have found great benefits in these cases when done in VR. While medical imaging on the desktop has lots of available software with various tools, VR versions are mostly stripped-down with only basic tools. One of the many tool groups significantly missing is annotation. In this paper, we survey the current situation of medical imaging software both on the desktop and in the VR environment. We will discuss general information on medical imaging and provide examples of both desktop and VR applications. We will also discuss the current status of annotation in VR, the problems that need to be overcome and possible solutions for them. The findings of this paper should help developers of future medical image annotation tools in choosing which problem they want to tackle and possible methods. The findings will be used to help in our future work of developing annotation tools. [ABSTRACT FROM AUTHOR]

Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring, and surgical planning due to the complex anatomical structure and physiological functions of the liver. This paper provides a comprehensive review of the developments, challenges, and future directions in liver segmentation technology. We systematically analyzed high-quality research published between 2014 and 2024, focusing on liver segmentation methods, public datasets, and evaluation metrics. This review highlights the transition from manual to semi-automatic and fully automatic segmentation methods, describes the capabilities and limitations of available technologies, and provides future outlooks. [ABSTRACT FROM AUTHOR]

Blockchain technology (BCT) is an emerging technology that has been used mainly in supply chain and financial technology. However, the use of this technology in Medical Imaging (MI) is still limited. This study investigates the acceptability of BCT in MI in public hospitals in Iraq. Based on relevant theories, the study proposed that Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), and Facilitating Condition (FC) significantly affect the acceptability of BCT in MI. Similarly, EE, PE, SI, and FC are expected to affect Initial Trust (IT), which in turn is proposed to mediate the effect of EE, PE, SI, and FC on the acceptability of BCT in MI. Data were collected from 136 doctors from public hospitals in Iraq. The results indicated that EE, PE, and SI positively affected the acceptability of BCT in MI. EE and PE positively affected IT. Furthermore, IT positively affected the acceptability of BCT in MI and mediated the effect of EE and PE. The study offers valuable insights for both theoretical and practical implementations, can guide future research, and informs strategies for the effective acceptability of BCT in MI in public hospitals. [ABSTRACT FROM AUTHOR]

Purpose: Calibration of an optical see-through head-mounted display is critical for augmented reality-based surgical navigation. While conventional methods have advanced, calibration errors remain significant. Moreover, prior research has focused primarily on calibration accuracy and procedure, neglecting the impact on the overall surgical navigation system. Consequently, these enhancements do not necessarily translate to accurate augmented reality in the optical see-through head mount due to systemic errors, including those in calibration. Method: This study introduces a simulated augmented reality-based calibration to address these issues. By replicating the augmented reality that appeared in the optical see-through head mount, the method achieves calibration that compensates for augmented reality errors, thereby reducing them. The process involves two distinct calibration approaches, followed by adjusting the transformation matrix to minimize displacement in the simulated augmented reality. Results: The efficacy of this method was assessed through two accuracy evaluations: registration accuracy and augmented reality accuracy. Experimental results showed an average translational error of 2.14 mm and rotational error of 1.06° across axes in both approaches. Additionally, augmented reality accuracy, measured by the overlay regions' ratio, increased to approximately 95%. These findings confirm the enhancement in both calibration and augmented reality accuracy with the proposed method. Conclusion: The study presents a calibration method using simulated augmented reality, which minimizes augmented reality errors. This approach, requiring minimal manual intervention, offers a more robust and precise calibration technique for augmented reality applications in surgical navigation. [ABSTRACT FROM AUTHOR]

Microscopic examination of tissues to detect oral cancer falls short as traditional microscopes struggle to easily differentiate between cancerous and non-cancerous cells. The identification of cancerous cells through microscopic biopsy images has the potential to alleviate concerns and improve outcomes if precise biological approaches are employed. However, relying solely on physical examinations and microscopic biopsy images for cancer identification increases the likelihood of human error and mistakes. Therefore, in order to obtain accurate results, a new research technique has been developed. In this manuscript, Gazelle Optimized Visual Geometry Group Network with Resnet101 fostered Oral Squamous Cell Carcinoma Detection (OCDVGGNetCNN-GOA-Resnet101) is proposed. In this method initially, the images are taken from Kaggle repository benchmark dataset and preprocessed to improve image quality. Then the result is given to the Visual Geometry group Network based CNN (VGGNetCNN) with Resnet101 for classification. Finally, the VGGNetCNN -ResNet 101 classifies image into normal and OSCC. Then the simulation performance of the proposed -VGGNetCNN-GOA-Resnet101 method attains 23.67%, 34.89%, 39.45% and 45.31% higher accuracy while compared with existing methods such as OCD-CNN-Alexnet, OCD-CNN-VGG19 and HIOCD-CNN-INet respectively. [ABSTRACT FROM AUTHOR]

The field of artificial intelligence (AI) and machine learning (ML) has been expanding and is explored by researchers in various fields. In medical diagnosis, for instance, the field of AI/ML is being explored because if medical diagnostic devices are built and designed with a backend of AI/ML, then the benefits would be unprecedented. Automated diagnostic tools would result in reduced health care costs, diagnosis without human intervention, overcoming human errors, and providing adequate and affordable medical care to a wider portion of the population with portions of the actual cost. One domain where AI/ML can make an immediate impact is medical imaging diagnosis (MID), namely the classification of medical images, where researchers have applied optimization techniques aiming to improve image classification accuracy. In this paper, we provide the research community with a comprehensive review of the most relevant studies to date on the use of deep CNN architecture optimization techniques for MID. As a case study, the application of these techniques to COVID-19 medical images were made. The impacts of the related variables, including datasets and AI/ML techniques, were investigated in detail. Additionally, the significant shortcomings and challenges of the techniques were touched upon. We concluded our work by affirming that the application of AI/ML techniques for MID will continue for many years to come, and the performance of the AI/ML classification techniques will continue to increase. [ABSTRACT FROM AUTHOR]

In the realm of disease prognosis and diagnosis, a plethora of medical images are utilized. These images are typically stored either within the local on-premises servers of healthcare providers or within cloud storage infrastructures. However, this conventional storage approach often incurs high infrastructure costs and results in sluggish information retrieval, ultimately leading to delays in diagnosis and consequential wastage of valuable time for patients. The methodology proposed in this paper offers a pioneering solution to expedite the diagnosis of medical conditions while simultaneously reducing infrastructure costs associated with data storage. Through this study, a high-speed biomedical image processing approach is designed to facilitate rapid prognosis and diagnosis. The proposed framework includes Deep learning QR code technique using an optimized database design aimed at alleviating the burden of intensive on-premises database requirements. The work includes medical dataset from Crawford Image and Data Archive and Duke CIVM for evaluating the proposed work suing different performance metrics, The work has also been compared from the previous research further enhancing the system's efficiency. By providing healthcare providers with high-speed access to medical records, this system enables swift retrieval of comprehensive patient details, thereby improving accuracy in diagnosis and supporting informed decision-making. [ABSTRACT FROM AUTHOR]

Deep learning and advancements in computer vision offer significant potential for analyzing medical images resulting in better healthcare and improved patient outcomes. Currently, the dominant approaches in the field of machine learning are supervised learning and transfer learning. These methods are not only prevalent in medicine and healthcare but also across various other industries. They rely on large datasets that have been manually annotated to train increasingly sophisticated models. However, the manual labeling process results in a wealth of untapped, unlabeled data that is accessible in both public and private data repositories. Self-supervised learning (SSL), an emerging field within machine learning, provides a solution by leveraging this untapped, unlabeled data. Unlike traditional machine learning paradigms, SSL algorithms pre-train models using artificial supervisory signals generated from the unlabeled data. This comprehensive review article explores the fundamental concepts, approaches, and advancements in self-supervised learning, with a particular emphasis on medical image datasets and their sources. By summarizing and highlighting the main contributions and findings from the article, this analysis and synthesis aim to shed light on the current state of research in self-supervised learning. Through these rigorous efforts, the existing body of knowledge is synthesized, and implementation recommendations are provided for future researchers interested in harnessing self-supervised learning to develop classification models for medical imaging. [ABSTRACT FROM AUTHOR]

For its vital role in maintaining cellular activity and survival, mitochondrion is highly involved in various diseases, and several strategies to target mitochondria have been developed for specific imaging and treatment. Among these approaches, theranostic may realize both diagnosis and therapy with one integrated material, benefiting the simplification of treatment process and candidate drug evaluation. A variety of mitochondria‐targeting theranostic agents have been designed based on the differential structure and composition of mitochondria, which enable more precise localization within cellular mitochondria at disease sites, facilitating the unveiling of pathological information while concurrently performing therapeutic interventions. Here, progress of mitochondria‐targeting theranostic materials reported in recent years along with background information on mitochondria‐targeting and therapy have been briefly summarized, determining to deliver updated status and design ideas in this field to readers. [ABSTRACT FROM AUTHOR]

Liver fibrosis is a reparative response triggered by liver injury. Non‐invasive assessment and staging of liver fibrosis in patients with chronic liver disease are of paramount importance, as treatment strategies and prognoses depend significantly on the degree of fibrosis. Although liver fibrosis has traditionally been staged through invasive liver biopsy, this method is prone to sampling errors, particularly when biopsy sizes are inadequate. Consequently, there is an urgent clinical need for an alternative to biopsy, one that ensures precise, sensitive, and non‐invasive diagnosis and staging of liver fibrosis. Non‐invasive imaging assessments have assumed a pivotal role in clinical practice, enjoying growing popularity and acceptance due to their potential for diagnosing, staging, and monitoring liver fibrosis. In this comprehensive review, we first delved into the current landscape of non‐invasive imaging technologies, assessing their accuracy and the transformative impact they have had on the diagnosis and management of liver fibrosis in both clinical practice and animal models. Additionally, we provided an in‐depth exploration of recent advancements in ultrasound imaging, computed tomography imaging, magnetic resonance imaging, nuclear medicine imaging, radiomics, and artificial intelligence within the field of liver fibrosis research. We summarized the key concepts, advantages, limitations, and diagnostic performance of each technique. Finally, we discussed the challenges associated with clinical implementation and offer our perspective on advancing the field, hoping to provide alternative directions for the future research. [ABSTRACT FROM AUTHOR]

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarm intelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC), K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and K-means with Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer Action Program (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics: precision, sensitivity, specificity, f-measure, accuracy, Matthews Correlation Coefficient (MCC), Jaccard, and Dice. The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved the quality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancer images. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and F-measure of 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove that K-means with ABC, K-means with PSO, K-means with FFA, and K-means with CSA have achieved an improvement of 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentation for lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significant improvement in accuracy, hence can be utilized by researchers for improved segmentation processes of medical image datasets for identifying the targeted region of interest. [ABSTRACT FROM AUTHOR]

Accurate and automatic detection of tiny lesions in medical imaging plays a critical role in comprehensive cancer diagnosis, staging, treatment, follow-up, and prognosis. Numerous deep learning (DL) based studies have focused on single lesions, providing highly sensitive identification and precise localization. On the other hand, some recent studies have started to concentrate on whole-body lesions, as they could provide systemic clinical support. This paper presents a single-to-universal review of DL studies on tiny lesion detection in medical imaging, with a particular emphasis on detection models and techniques, as well as the data-related aspects such as modality, dimension, and dataset. A wide range of tasks are covered, including traditional single lesion detection tasks such as lung nodules, breast masses, thyroid nodules, and diseased lymph nodes, as well as the emerging task of universal lesion detection. Moreover, the paper provides in-depth analysis and discussion, with the hope of inspiring future research endeavors. [ABSTRACT FROM AUTHOR]

Throughout the history of human spaceflight, spacefarers have experienced and reported the occurrence ofmedical conditions, including various illnesses and injuries. Therefore, future spaceflight missions to the Moon and Mars will require the capabilities necessary for maintaining the health of these new space travelers. Mass, power, and volume available in the space vehicles used for these missions will be severely constrained. The ability to resupply or evacuate to Earth will be limited or non-existent, and ground-based support will no longer be immediate due to communication latencies and blackouts. These vehicle and mission constraints will necessitate healthcare be provided from an efficiently planned medical system. To provide the necessary care, thesemedical systemswill need to include at aminimum, several different types of medical devices, consumable resources, centralized data management, procedural guidance, and decision support technologies. Medical devices needed for diagnosing and treating medical conditions that are expected to occur during future spaceflight missions may include real-time health monitoring, medical imaging capabilities, as well as blood and urine analysis. Novel methods for interacting with onboard patient medical records will be necessary, as will resource tracking. Terrestrial medicine sharesmany of these same needs, therefore a multitude of these required medical capabilities can likely be satisfied by currently available, Commercial-Off-The-Shelf (COTS) devices and methodologies; however, in some cases the unique space environment and increased mission durations will drive the need for modifications or customization of standard technologies and treatment procedures. This article will provide a review of medical devices and technologies that have been considered for inclusion within future spaceflight medical systems. It will also include a discussion about the modifications and customized development that have been performed, as well as descriptions of the technology demonstrations that have been conducted in analog and spaceflight environments. [ABSTRACT FROM AUTHOR]