Diagnostic Tasks Research Articles

A segmentation-based algorithm for classification of benign and malignancy Thyroid nodules with multi-feature information.

The aim of this study is to propose a new diagnostic model based on "segmentation + classification" to improve the routine screening of Thyroid nodule ultrasonography by utilizing the key domain knowledge of medical diagnostic tasks. A Multi-scale segmentation network based on a pyramidal pooling structure of multi-parallel void spaces is proposed. First, in the segmentation network, the exact information of the underlying feature space is obtained by an Attention Gate. Second, the inflated convolutional part of Atrous Spatial Pyramid Pooling (ASPP) is cascaded for multiple downsampling. Finally, a three-branch classification network combined with expert knowledge is designed, drawing on doctors' clinical diagnosis experience, to extract features from the original image of the nodule, the regional image of the nodule, and the edge image of the nodule, respectively, and to improve the classification accuracy of the model by utilizing the Coordinate attention (CA) mechanism and cross-level feature fusion. The Multi-scale segmentation network achieves 94.27%, 93.90% and 88.85% of mean precision (mPA), Dice value (Dice) and mean joint intersection (MIoU), respectively, and the accuracy, specificity and sensitivity of the classification network reaches 86.07%, 81.34% and 90.19%, respectively. Comparison tests show that this method outperforms the U-Net, AGU-Net and DeepLab V3+ classical models as well as the nnU-Net, Swin UNetr and MedFormer models that have emerged in recent years. This algorithm, as an auxiliary diagnostic tool, can help physicians more accurately assess the benign or malignant nature of Thyroid nodules. It can provide objective quantitative indicators, reduce the bias of subjective judgment, and improve the consistency and accuracy of diagnosis. Codes and models are available at https://github.com/enheliang/Thyroid-Segmentation-Network.git.

USING A WIRELESS SENSOR NODE TO MEASURE THE ACCELERATION OF THE SWING ROCKING MACHINE NODES

Technical diagnosis of the drive of well rod pump units (WRPU) remains an urgent task, despite the significant amount of research in this field. One of the areas of diagnosis is the measurement and control of the angular velocity of the rocking machine elements, which requires the provision of synchronous registration and control of acceleration signals at many points of the control object. The authors proposed to implement such a system based on the wireless sensor module Steval-MKSBOX1V1 of STMicroelectronics. The results of the experimental approbation of the use of the specified module showed the effectiveness of its use for solving diagnostic tasks, since the module contains a significant set of sensors in combination with a unit for processing, storing and transmitting information via a wireless Bluetooth interface. An additional advantage of using the selected measuring module is the possibility of remote reprogramming of the module's software to perform the tasks of controlling the kinematic parameters of the installation in the process of conducting research directly at the technological facility. The use of three coordinate accelerometers as part of the measuring module allows to significantly increase the reliability of control in the diagnostic process.

Few-Shot Synthetic Online Transfer Learning for Cross-Site Neurological Disease Diagnosis

Cross-site datasets expand the data size and could improve the disease diagnosis capabilities of machine learning models. However, differences in data distribution between different sites can lead to poor model generalizability. Although transfer learning is a mainstream method often used to tackle the issue, most transfer learning studies assume that all the target samples are given in the training procedure, which is not available in clinical applications where the target samples arrive sequentially. Online transfer learning (OTL) aims to accomplish clinical diagnostic tasks by adaptively updating the ensemble model containing source and target classifiers. However, OTL is limited by zero initialization and requires numbers of samples to iterate. This results in the underperformance of OTL in clinical applications where few samples can be obtained. In this article, we propose a new framework named few-shot synthetic OTL (FSOTL) to address this issue. FSOTL uses synthetic data to warm up the model in an online fashion. It not only alleviates the problem of scarcity of samples in the target domain but also enables the model to gain more knowledge. Our experiments show that FSOTL performs more stably and achieves more accurate results with few target samples, thereby offering a promising cross-site online computer-aided diagnosis system for large-scale applications.

The effects of gaze-display feedback on medical students’ self-monitoring and learning in radiology

Self-monitoring is essential for effectively regulating learning, but difficult in visual diagnostic tasks such as radiograph interpretation. Eye-tracking technology can visualize viewing behavior in gaze displays, thereby providing information about visual search and decision-making. We hypothesized that individually adaptive gaze-display feedback improves posttest performance and self-monitoring of medical students who learn to detect nodules in radiographs. We investigated the effects of: (1) Search displays, showing which part of the image was searched by the participant; and (2) Decision displays, showing which parts of the image received prolonged attention in 78 medical students. After a pretest and instruction, participants practiced identifying nodules in 16 cases under search-display, decision-display, or no feedback conditions (n = 26 per condition). A 10-case posttest, without feedback, was administered to assess learning outcomes. After each case, participants provided self-monitoring and confidence judgments. Afterward, participants reported on self-efficacy, perceived competence, feedback use, and perceived usefulness of the feedback. Bayesian analyses showed no benefits of gaze displays for post-test performance, monitoring accuracy (absolute difference between participants’ estimated and their actual test performance), completeness of viewing behavior, self-efficacy, and perceived competence. Participants receiving search-displays reported greater feedback utilization than participants receiving decision-displays, and also found the feedback more useful when the gaze data displayed was precise and accurate. As the completeness of search was not related to posttest performance, search displays might not have been sufficiently informative to improve self-monitoring. Information from decision displays was rarely used to inform self-monitoring. Further research should address if and when gaze displays can support learning.

MEDICAL FUZZY-EXPERT SYSTEM FOR PREDICTION OF ENGRAFTMENT DEGREE OF DENTAL IMPLANTS IN PATIENTS WITH CHRONIC LIVER DISEASE

The paper presents an information technology for assessing the degree of engraftment of dental implants in the event of a pathology violation through the use of fuzzy sets, which allows using this method for medical diagnostic tasks. Main scientific results: developed algorithms and mathematical models that formalize the process supporting diagnostic decisions based on fuzzy logic; developed mathematical models of membership functions that formalize the presentation of qualitative and qualitative informational features based on the rules of fuzzy logic, which can be used in information expert systems when assessing the degree of engraftment of dental implants in case of disease with pathological diseases.

The selective deployment of AI in healthcare: An ethical algorithm for algorithms.

Machine-learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in health care, yet algorithmic performance levels can be materially worse for subgroups that have been underrepresented in algorithmic training data. Given this epistemic deficit, the inclusion of underrepresented groups in algorithmic processes can result in harm. Yet delaying the deployment of algorithmic systems until more equitable results can be achieved would avoidably and foreseeably lead to a significant number of unnecessary deaths in well-represented populations. Faced with this dilemma between equity and utility, we draw on two case studies involving breast cancer and melanoma to argue for the selective deployment of diagnostic and prognostic tools for some well-represented groups, even if this results in the temporary exclusion of underrepresented patients from algorithmic approaches. We argue that this approach is justifiable when the inclusion of underrepresented patients would cause them to be harmed. While the context of historic injustice poses a considerable challenge for the ethical acceptability of selective algorithmic deployment strategies, we argue that, at least for the case studies addressed in this article, the issue of historic injustice is better addressed through nonalgorithmic measures, including being transparent with patients about the nature of the current epistemic deficits, providing additional services to algorithmically excluded populations, and through urgent commitments to gather additional algorithmic training data from excluded populations, paving the way for universal algorithmic deployment that is accurate for all patient groups. These commitments should be supported by regulation and, where necessary, government funding to ensure that any delays for excluded groups are kept to the minimum. We offer an ethical algorithm for algorithms-showing when to ethically delay, expedite, or selectively deploy algorithmic systems in healthcare settings.

Magnetic Resonance Imaging in Objective Response Evaluation in Klatskin Tumor Patients after Endobiliary Photodynamic Therapy

Purpose: Visualization of periductal infiltrative hilar cholangiocarcinomas in general and their changes in response to endobiliary photodynamic therapy (PDT) in particular is not an easy diagnostic task because of their small size, infiltrative growth pattern, and absence of clearly circumscribed margins with adjacent tissue. This caused most researchers to give up the study of criteria for objective response to locoregional treatment, and to use only the survival rate as performance criteria. The aim of this study is to determine the criteria of objective response in Klatskin tumor after PDT based on the analysis of preand postoperative MRI.Material and methods: The study covered 42 patients (26 male (62 %) and 16 female (38 %)) with morphologically verified Klatskin tumor aged 21-76 (median 55) after 104 sessions (from 1 to 10, median 2) of percutaneous endobiliary PDT over the period of 2007-2020. All patients had percutaneous transhepatic or intraoperative drainage of bile ducts from 1 week to 2 years before PDT. Chlorine photosensitizers were used for PDT. The LAHTA-MILON programmable laser unit with 662 nm wavelength was used for photo-irradiation. The specific dose varied from 4.6 to 232,2 J/cm2 (median 29) and the total dose was 64.6-3890 J (median 950), depending on the extent of lesion and the patient’s tolerance to interventions. 220 abdominal MRI were done at various times before and after PDT. Preoperative MRI were completed 1-101 days (median 13.5 days) before the intervention. Postoperative MRI scans were split into groups based on the time following the intervention: 1) after 1–5 days, 2) after 2 months, 3) after 3 months, and 4) after 4 months. Examinations were carried out with the Siemens Magnetom Avanto/Espree 1.5 Tesla MRI scanner. To compare the indicators at different times, the Kruskal–Wallis test was used, p < 0.05 were considered significant.Results: As a result of the analysis, the following statistically significant changes in the Klatskin tumor after PDT were obtained at various times: the largest transverse size of the tumor infiltrate decrease at the proximal hepaticocholedochus level 1 month after PDT; the infiltrate longitudinal size increase at the bile ducts confluence level 4 month after PDT; T2-WI signal intensity increase at the photodynamic area in 1–5 days after PDT; T1-WI signal intensity decrease in various contrasting phases at the photodynamic area in 1–5 days after PDT; ADC signal intensity increase at the photodynamic and conditionally healthy tissues areas 1 month after PDT; T1-WI signal intensity increase in venous and delayed contrasting phases at the photodynamic area 1 month after PDT; T1-WI signal intensity decrease in porto-venous and delayed contrasting phases at the photodynamic area 2 month after PDT; ADC signal intensity decrease at conditionally healthy tissues 4 month after PDT.Conclusion: Changes in MR signal intensity after endobiliary PDT with different pulse sequences (DWI + ADC, T2-WI with and without suppression of signal from adipose tissue, T1-WI before and after intravenous injection of contrast agent in 4 phases of contrasting) in pathological and conditionally healthy intraand periductal tissues and the tumor dimensions (largest crosssectional dimensions and thickness measured in axial and coronal projections in T2-WI) may be used as the criteria of objective response. Determination in the early postoperative period of periductal segments in the area of photodynamic treatment that do not accumulate contrast agent is the most informative, displayable and convenient for practical use criterion of objective response indicating the ischemia of tumor tissue.

Resilience-aware MLOps for AI-based medical diagnostic system.

The healthcare sector demands a higher degree of responsibility, trustworthiness, and accountability when implementing Artificial Intelligence (AI) systems. Machine learning operations (MLOps) for AI-based medical diagnostic systems are primarily focused on aspects such as data quality and confidentiality, bias reduction, model deployment, performance monitoring, and continuous improvement. However, so far, MLOps techniques do not take into account the need to provide resilience to disturbances such as adversarial attacks, including fault injections, and drift, including out-of-distribution. This article is concerned with the MLOps methodology that incorporates the steps necessary to increase the resilience of an AI-based medical diagnostic system against various kinds of disruptive influences. Post-hoc resilience optimization, post-hoc predictive uncertainty calibration, uncertainty monitoring, and graceful degradation are incorporated as additional stages in MLOps. To optimize the resilience of the AI based medical diagnostic system, additional components in the form of adapters and meta-adapters are utilized. These components are fine-tuned during meta-training based on the results of adaptation to synthetic disturbances. Furthermore, an additional model is introduced for post-hoc calibration of predictive uncertainty. This model is trained using both in-distribution and out-of-distribution data to refine predictive confidence during the inference mode. The structure of resilience-aware MLOps for medical diagnostic systems has been proposed. Experimentally confirmed increase of robustness and speed of adaptation for medical image recognition system during several intervals of the system's life cycle due to the use of resilience optimization and uncertainty calibration stages. The experiments were performed on the DermaMNIST dataset, BloodMNIST and PathMNIST. ResNet-18 as a representative of convolutional networks and MedViT-T as a representative of visual transformers are considered. It is worth noting that transformers exhibited lower resilience than convolutional networks, although this observation may be attributed to potential imperfections in the architecture of adapters and meta-adapters. The main novelty of the suggested resilience-aware MLOps methodology and structure lie in the separating possibilities and activities on creating a basic model for normal operating conditions and ensuring its resilience and trustworthiness. This is significant for the medical applications as the developer of the basic model should devote more time to comprehending medical field and the diagnostic task at hand, rather than specializing in system resilience. Resilience optimization increases robustness to disturbances and speed of adaptation. Calibrated confidences ensure the recognition of a portion of unabsorbed disturbances to mitigate their impact, thereby enhancing trustworthiness.

A comprehensive lung CT landmark pair dataset for evaluating deformable image registration algorithms.

Deformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high-quality benchmark datasets by which new DIR algorithms can be evaluated. Our team was supported by the National Institute of Biomedical Imaging and Bioengineering to develop DIR benchmark dataset libraries for multiple anatomical sites, comprising of large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Here we introduce our lung CT DIR benchmark dataset library, which was developed to improve upon the number and distribution of landmark pairs in current public lung CT benchmark datasets. Thirty CT image pairs were acquired from several publicly available repositories as well as authors' institution with IRB approval. The data processing workflow included multiple steps: (1) The images were denoised. (2) Lungs, airways, and blood vessels were automatically segmented. (3) Bifurcations were directly detected on the skeleton of the segmented vessel tree. (4) Falsely identified bifurcations were filtered out using manually defined rules. (5) A DIR was used to project landmarks detected on the first image onto the second image of the image pair to form landmark pairs. (6) Landmark pairs were manually verified. This workflow resulted in an average of 1262 landmark pairs per image pair. Estimates of the landmark pair target registration error (TRE) using digital phantoms were 0.4mm±0.3mm. The data is published in Zenodo at https://doi.org/10.5281/zenodo.8200423. Instructions for use can be found at https://github.com/deshanyang/Lung-DIR-QA. The dataset library generated in this work is the largest of its kind to date and will provide researchers with a new and improved set of ground truth benchmarks for quantitatively validating DIR algorithms within the lung.

Influence of notebook computers screens and undergraduate level of dental students in the radiographic detection of carious lesions

Aim: To evaluate the influence of notebook computers screens and undergraduate level of dental students in the radiographic detection of carious lesions. Methods: Bitewing digital radiographs were presented to 3rd and 5th year dental students in three different notebooks computers: Notebook 1 with anti-glare screen (1366×768 pixels), Notebook 2 without anti-glare screen (1366×768 pixels), and Notebook 3 with anti-glare screen (1920×1080 pixels). A reference standard based on a consensus analysis was set by three senior professors of Oral Radiology and Cariology. Sensitivity, specificity and accuracy values were measured and submitted to two-way ANOVA at a significance level of 5%. Results: Notebook 2 provided significantly lower sensitivity values (Mean 56.5% ± 2.94) than notebook 3 (71.1% ± 2.82) (p = 0.002). We found no statistically significant differences between the two undergraduate years (p > 0.05). Conclusion: The anti-glare screen of notebook computers screens can influence the radiographic detection of carious lesions, but the undergraduate level of dental students does not influence this diagnostic task.

Potential of Artificial Intelligence to Generate Health Research Reports of Decayed, Missed and Restored Teeth

This study aims to indicate the potential of artificial intelligence (AI) in epidemiological reports of decayed, missed and restored teeth. As a proof of concept our study model used panoramic x-ray images and an AI algorithm for tooth numbering, detection of the caries and restorations with accuracy over 80% for such diagnostic tasks. The output came as the number of decayed, missed and restored teeth according to patient´s age and the DMFT index (number of decayed, missing, and filled teeth) which varied from 3.6 (up to 20 years old) to 20.4 (+60 years old). Thus, it is suggested that AI is a promising method to automate health data collection through the analysis of x-rays.

The role of artificial intelligence in modern ophthalmology

Currently, artificial intelligence is actively being introduced into various spheres of life, and medicine is no exception. In ophthalmology, the use of artificial intelligence is very promising, given that the diagnosis and therapeutic monitoring of eye diseases often depend heavily on the correct interpretation of images. The use of artificial intelligence in ophthalmology focuses on eye diseases that lead to vision loss, such as age-related macular degeneration, diabetic retinopathy, glaucoma and cataract. Over the past few years, artificial intelligence has reached tremendous successes in the practice of ophthalmology. Many studies have shown that artificial intelligence performance is equal to and even exceeds the capabilities of ophthalmologists in many diagnostic and prognostic tasks. However, there is still a lot of work to be done before introducing artificial intelligence into routine clinical practice. Issues such as real-world performance, generalizability, and interpretability of artificial intelligence systems are still poorly understood and will require more attention in future research. Most artificial intelligence-based systems are used in developed countries, and some require further study. High costs and a shortage in doctors and equipment in some regions of the Russian Federation and rural areas make it difficult to screen for eye diseases. Although the field of artificial intelligence is underdeveloped, we hope that artificial intelligence will play an important role in the future of ophthalmology by making healthcare more efficient, accurate and accessible, especially in regions where staffing problems exist.

Hybrid Quantum Image Classification and Federated Learning for Hepatic Steatosis Diagnosis.

In the realm of liver transplantation, accurately determining hepatic steatosis levels is crucial. Recognizing the essential need for improved diagnostic precision, particularly for optimizing diagnosis time by swiftly handling easy-to-solve cases and allowing the expert time to focus on more complex cases, this study aims to develop cutting-edge algorithms that enhance the classification of liver biopsy images. Additionally, the challenge of maintaining data privacy arises when creating automated algorithmic solutions, as sharing patient data between hospitals is restricted, further complicating the development and validation process. This research tackles diagnostic accuracy by leveraging novel techniques from the rapidly evolving field of quantum machine learning, known for their superior generalization abilities. Concurrently, it addresses privacy concerns through the implementation of privacy-conscious collaborative machine learning with federated learning. We introduce a hybrid quantum neural network model that leverages real-world clinical data to assess non-alcoholic liver steatosis accurately. This model achieves an image classification accuracy of 97%, surpassing traditional methods by 1.8%. Moreover, by employing a federated learning approach that allows data from different clients to be shared while ensuring privacy, we maintain an accuracy rate exceeding 90%. This initiative marks a significant step towards a scalable, collaborative, efficient, and dependable computational framework that aids clinical pathologists in their daily diagnostic tasks.

Systematic analysis of ChatGPT, Google search and Llama 2 for clinical decision support tasks

It is likely that individuals are turning to Large Language Models (LLMs) to seek health advice, much like searching for diagnoses on Google. We evaluate clinical accuracy of GPT-3·5 and GPT-4 for suggesting initial diagnosis, examination steps and treatment of 110 medical cases across diverse clinical disciplines. Moreover, two model configurations of the Llama 2 open source LLMs are assessed in a sub-study. For benchmarking the diagnostic task, we conduct a naïve Google search for comparison. Overall, GPT-4 performed best with superior performances over GPT-3·5 considering diagnosis and examination and superior performance over Google for diagnosis. Except for treatment, better performance on frequent vs rare diseases is evident for all three approaches. The sub-study indicates slightly lower performances for Llama models. In conclusion, the commercial LLMs show growing potential for medical question answering in two successive major releases. However, some weaknesses underscore the need for robust and regulated AI models in health care. Open source LLMs can be a viable option to address specific needs regarding data privacy and transparency of training.

Small Robot Vehicle Developed for Multi-purpose Industrial Use

The small robotic vehicle presented in this article is a special, multifunctional vehicle that can perform diagnostic tasks in several different areas. Examination of heat release and heat losses, which characterizes the efficiency of processes in many technical areas. A device that penetrates into small spaces may be needed. In this area, an important task is to navigate the device to the right place under trucks and cars to perform thermal camera diagnostics. The examination of excessive heat emission is extremely important to filter out faulty transmission systems, faulty engines, and to detect excessively hot brake drums, which are unfortunately also responsible for causing many serious accidents and fires.The remote-controlled navigation robot with a camera presented in the article is perfectly suited for the above task, as it can also transmit the image of the thermal camera to a remote display. In addition to automotive applications, the device can also be used for other industrial purposes and machine diagnostic tasks. I According to the current European Union building energy regulations, new buildings must have almost zero energy requirements. The so-called thermal bridges must be calculated for both old and new buildings under construction.A useful solution to this problem can be the quick and cost-effective energetic measurement and classification of thermal bridges, factors, and structural elements that cause heat loss. The Heat Spotter robotic vehicle presented and built in the article is suitable for the above tasks, ready to be manufactured and quickly paying for itself.

Photon-counting CT in Thoracic Imaging: Early Clinical Evidence and Incorporation Into Clinical Practice.

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical studies examining the benefit of PCCT relative to conventional CT and integration of PCCT systems into multivendor, multispecialty radiology practices are discussed.

Heterogeneity and predictors of the effects of AI assistance on radiologists

The integration of artificial intelligence (AI) in medical image interpretation requires effective collaboration between clinicians and AI algorithms. Although previous studies demonstrated the potential of AI assistance in improving overall clinician performance, the individual impact on clinicians remains unclear. This large-scale study examined the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks and identified predictors of these effects. Surprisingly, conventional experience-based factors, such as years of experience, subspecialty and familiarity with AI tools, fail to reliably predict the impact of AI assistance. Additionally, lower-performing radiologists do not consistently benefit more from AI assistance, challenging prevailing assumptions. Instead, we found that the occurrence of AI errors strongly influences treatment outcomes, with inaccurate AI predictions adversely affecting radiologist performance on the aggregate of all pathologies and on half of the individual pathologies investigated. Our findings highlight the importance of personalized approaches to clinician–AI collaboration and the importance of accurate AI models. By understanding the factors that shape the effectiveness of AI assistance, this study provides valuable insights for targeted implementation of AI, enabling maximum benefits for individual clinicians in clinical practice.

Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach.

Mild cognitive impairment plays a crucial role in predicting the early progression of Alzheimer's disease, and it can be used as an important indicator of the disease progression. Currently, numerous studies have focused on utilizing the functional brain network as a novel biomarker for mild cognitive impairment diagnosis. In this context, we employed a graph convolutional neural network to automatically extract functional brain network features, eliminating the need for manual feature extraction, to improve the mild cognitive impairment diagnosis performance. However, previous graph convolutional neural network approaches have primarily concentrated on single modes of brain connectivity, leading to a failure to leverage the potential complementary information offered by diverse connectivity patterns and limiting their efficacy. To address this limitation, we introduce a novel method called the graph convolutional neural network with multimodel connectivity, which integrates multimode connectivity for the identification of mild cognitive impairment using fMRI data and evaluates the graph convolutional neural network with multimodel connectivity approach through a mild cognitive impairment diagnostic task on the Alzheimer's Disease Neuroimaging Initiative dataset. Overall, our experimental results show the superiority of the proposed graph convolutional neural network with multimodel connectivity approach, achieving an accuracy rate of 92.2% and an area under the Receiver Operating Characteristic (ROC) curve of 0.988.

Automated Cardiovascular Disease Diagnosis from X-ray Images using Convolutional Neural Networks

Problem Cardiovascular Disease (CVD) is a leading cause of mortality worldwide, and its early and accurate diagnosis is crucial for effective treatment and patient care. Medical imaging, particularly X-ray imaging, plays a crucial role in the detection and assessment of cardiovascular abnormalities. In recent years, Convolutional Neural Networks (CNNs) have emerged as a powerful tool in medical image analysis, demonstrating promising results in various diagnostic tasks. Proposed Approach This research paper investigates the application of CNNs for the automated diagnosis of CVD from X-ray images. The CVD diagnosis framework proposed in this study consists of three key modules. The first module is an X-ray feature extractor built using a state-of-the-art CNN architecture. The second module is an age prediction component, which accurately estimates the age of the patients from the X-ray images. Finally, the third module is the CVD classifier, which categorizes the input X-ray images into four predefined severity categories of Cardiovascular Disease. Result Through extensive experiments, the proposed method has demonstrated its capability to offer novel insights into the potential use of X-ray images for predicting systemic biomarkers in the diagnosis of CVD. I expect that the proposed CVD diagnosis method can provide a significant advancement in the field of cardiovascular healthcare by offering an accurate, efficient, and automated solution for early detection of CVD.

Gradient-based domain-augmented meta-learning single-domain generalization for fault diagnosis under variable operating conditions

Equipment operating conditions, referred to as domains, can induce domain drift in monitoring data, affecting data-driven fault diagnosis. Researchers have explored multi-domain generalization methods to tackle this issue. However, in actual industrial scenarios, the availability of fault data may be limited to a specific condition due to the cost or feasibility constraints associated with collecting extensive monitoring data. This limitation hampers the generalization ability of these methods, posing a major challenge for robust fault diagnosis under variable operating conditions. To address this challenge, we proposed a gradient-based domain-augmented meta-learning (GDM) single-domain generalization method. We analyze the restrictions of generating fake domains and construct a domain-augmented loss by evaluating diagnostic tasks minimization, semantic consistency, and distribution diversity for fake samples. Using a gradient-based technique, fake domains are generated iteratively, providing diverse fault knowledge for improved generalization. Instead of using time-consuming ensemble methods, we develop a novel meta-learning method to train a highly efficient and generalizable model, relaxing the requirement for auxiliary datasets in existing meta-learning methods. Two case studies consistently demonstrate the effectiveness and superiority of the proposed GDM method. Our findings suggest that this study offers a promising and competitive solution for single-domain generalization in fault diagnosis within real industrial scenarios.