Method For Medical Diagnosis Research Articles

Magnetic resonance imaging diagnosis of ankle joint athletic injury based on machine learning algorithms

The diagnosis of ankle joint athletic injuries using traditional magnetic resonance imaging (MRI) relies on the subjective judgment and experience of doctors, and small structural changes in athletic injuries are difficult to accurately detect and diagnose. By using machine learning (ML) algorithms and image processing techniques to obtain objective and consistent diagnostic results, the accuracy of diagnosing ankle joint athletic injuries can be improved. This article collected a large number of MRI images of ankle joint athletic injuries, and preprocessed the collected images to extract morphological and texture features, and perform feature fusion. The Residual Network (ResNet) was improved, and the Leaky linear rectification function (ReLU, Corrected linear unit) activation function was introduced. The transfer learning was utilized to increase the convergence speed of the model, and the global maximum pooling layer and softmax classifier were used to construct the fully connected layer. After sufficient training on the training set, the findings on the test set indicated that the average accuracy of the improved ResNet model for ankle joint injury classification was 98.3%. The use of an improved ResNet model can effectively improve the diagnostic effectiveness of ankle joint athletic injuries, providing a new method for medical diagnosis of MRI.

Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis

Accurate and early diagnosis, coupled with precise prognosis, is critical for improving patient outcomes in various medical conditions. This paper focuses on leveraging advanced data mining techniques to address two key medical challenges: diagnosis and prognosis. Diagnosis involves differentiating between benign and malignant conditions, while prognosis aims to predict the likelihood of recurrence after treatment. Despite significant advances in medical imaging and clinical data collection, achieving high accuracy in both diagnosis and prognosis remains a challenge. This study provides a comprehensive review of state-of-the-art machine learning and data mining techniques used for medical diagnosis and prognosis, including Neural Networks, K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, Decision Trees, and Support Vector Machines (SVM). These methods are evaluated on their ability to process large, complex datasets and produce actionable insights for medical practitioners.We conducted a thorough comparative analysis based on key performance metrics such as accuracy, Area Under the Curve (AUC), precision, recall, and specificity. Our findings reveal that Neural Networks consistently outperform other techniques in terms of diagnostic accuracy and predictive capacity, demonstrating their robustness in handling high-dimensional and nonlinear medical data. This research underscores the potential of advanced machine learning algorithms in revolutionizing early diagnosis and effective prognosis, thus facilitating more personalized treatment plans and improved healthcare outcomes.

Artificial Intelligence And Its Implications In Health Care: A Textual Analysis

This all-inclusive research paper plunges into the transformative nature of artificial intelligence (AI) in healthcare, emphasizing its ability to change patient care and administrative activities. The investigation examines systematically how AI technologies are creating major improvements towards patients’ outcomes and enhanced operational efficiency within the healthcare systems. From diagnostics to personalized treatment plans and administrative tasks, AI is being integrated using sophisticated algorithms in numerous aspects of healthcare delivery. One major focus is improving diagnostic accuracy. AI-backed diagnostic tools such as image recognition software as well as predictive models have outperformed traditional methods in medical condition identification and diagnosis. For instance, artificial intelligence algorithms show exceptional skills in analysing medical images resulting to earlier detection as well as accurate diagnoses of diseases like cancer, cardiovascular conditions among others neurological disorders. These enhancements not only allow for timely intervention but they significantly reduce instances where diagnosis goes wrong hence enhancing patient prognosis while reducing health costs drastically. The ability to customize personalized therapy is yet another essential aspect of AI in healthcare. With such information as genetic data, medical records and lifestyle factors, and other patient information, AI systems can develop individualized treatment plans. It is for this reason that personalization of treatments enhances their efficiency while reducing the risk of any adverse effects as well as ensuring patients’ adherence to prescribed therapies. Besides, it is important to note that AI-driven predictive models are helpful in identifying individuals with a higher probability of developing specific conditions thereby allowing interventions aimed at preventing diseases and bettering overall population health. AI has also transformed administrative tasks within hospitals besides its clinical applications. For example, AI driven automation is now used to streamline scheduling of appointments or billing processes or even storage of patient records which are normally considered routine administrative works. In addition, through these means there is reduced burden on administrative duties for healthcare workers who continue to perform them with increased precision and timeliness. The net effect is that time and resources can be redirected towards more direct patient care thus improving service quality across the board’s entire system

An Efficient and Robust 3D Medical Image Classification Approach Based on 3D CNN, Time‐Distributed 2D CNN‐BLSTM Models, and mRMR Feature Selection

ABSTRACTThe advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time‐consuming. In the last decades, artificial intelligence‐based computer‐aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time‐distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.

The morphology of epiretinal membranes in SS-OCT and SS-OCT angiography and its impact on surgical outcomes

The aim of this review is to revisit the current state of knowledge regarding epiretinal membranes, explore current methods of medical diagnosis, and assess the impact of Swept-source OCT (SS-OCT) and SS-OCT angiography (SS-OCT A) on therapeutic procedures. Several attempts have been made to establish a staging classification system for this well-known retinal pathology, yet not all of them proved to be clinically useful. This work intends to improve understanding of the pathophysiology of the disease, selecting the most important symptoms to aid classification according to the stage of advancement and correct recognition of the appropriate moment of surgical intervention to achieve a better postoperative effect. This article demonstrates the benefits of using SS-OCT and SS-OCT A in the treatment of epiretinal membrane, describing the morphological changes of the retina that can be observed.

A privacy-preserving expert system for collaborative medical diagnosis across multiple institutions using federated learning

Expert system recommendation assists the healthcare system to develop in real-time monitoring and diagnosis of patient conditions over several healthcare institutions. Privacy concerns, however, present significant problems since patient data leaks can lead to big effects including financial losses for hospitals and invasions of personal privacy for people. To address these issues, the research introduces a privacy-preserving collaborative medical diagnosis (CMD) method on a federated learning (FL). FL maintains patient privacy and data localization by spreading only model parameters, therefore enabling training models on remote datasets. The combination of Partially Homomorphic Cryptosystem (PHC) and Residual Learning based Deep Belief Network (RDBN) ensures an accurate and safe classification of patient physiological data. Experimental results show that the proposed method is successful in maintaining the diagnostic accuracy over numerous healthcare institutions and protecting privacy. The results show that the RDBN and PHC computations requires around 1000 ms and 150 ms, respectively for classification and privacy; the data transmission from the user to server and from server to user is 5 MB and 4 MB, respectively. Finally with a 30% reduction in overhead, the proposed approach offers an average increase in classification accuracy of 10% over multiple datasets.

Bipolar Fuzzy Soft Set Theory Applied to Medical Diagnosis

The primary objective of this study is to advance Sanchez’s method for medical diagnosis by incorporating fuzzy arithmetic operations. To achieve this, we generalize the existing approach through the application of bipolar fuzzy soft set theory, which enables the identification of two distinct types of medical knowledge within a unified framework. Additionally, we propose a novel decision-making algorithm tailored to this enhanced approach. The application of this algorithm in the medical field is illustrated through practical examples, demonstrating its potential to improve diagnostic processes and decision-making in medical practice.

Optimal extreme learning machine for diagnosing brain tumor based on modified sailfish optimizer

This study proposes a hierarchical automated methodology for detecting brain tumors in Magnetic Resonance Imaging (MRI), focusing on preprocessing images to improve quality and eliminate artifacts or noise. A modified Extreme Learning Machine is then used to diagnose brain tumors that are integrated with the Modified Sailfish optimizer to enhance its performance. The Modified Sailfish optimizer is a metaheuristic algorithm known for efficiently navigating optimization landscapes and enhancing convergence speed. Experiments were conducted using the “Whole Brain Atlas (WBA)” database, which contains annotated MRI images. The results showed superior efficiency in accurately detecting brain tumors from MRI images, demonstrating the potential of the method in enhancing accuracy and efficiency. The proposed method utilizes hierarchical methodology, preprocessing techniques, and optimization of the Extreme Learning Machine with the Modified Sailfish optimizer to improve accuracy rates and decrease the time needed for brain tumor diagnosis. The proposed method outperformed other methods in terms of accuracy, recall, specificity, precision, and F1 score in medical imaging diagnosis. It achieved the highest accuracy at 93.95 %, with End/End and CNN attaining high values of 89.24 % and 93.17 %, respectively. The method also achieved a perfect score of 100 % in recall, 91.38 % in specificity, and 75.64 % in F1 score. However, it is crucial to consider factors like computational complexity, dataset characteristics, and generalizability before evaluating the effectiveness of the method in medical imaging diagnosis. This approach has the potential to make substantial contributions to medical imaging and aid healthcare professionals in making prompt and precise treatment decisions for brain tumors.

Ultra-wideband Imaging of Breast Tumors Based on Global Back Projection Algorithm

Abstract Tumor imaging in medicine requires consideration of human safety, image quality clarity, and cost of imaging. Ultra-wideband (UWB) technology employs large bandwidth channels that enable high-speed data transmission. This determines that it can provide high-quality images quickly while still being a harmless imaging method. Therefore, it is essential to construct a UWB microwave detection tumor system. The creation of a vector network analyzer (VNA) based microwave imaging system in this work. To adapt to breast imaging, this paper improved the imaging algorithm of global back projection (GBP) based on the classical back projection algorithm. To verify the system’s feasibility, this paper performed UWB detection experiments using a breast tumor model. By comparing the simulation results, the final imaging results show that the UWB imaging system can detect tumors located at two different locations in the model and that high-quality images can be obtained in the near field using the GBP algorithm. This paper provides a reliable and low-cost method for medical diagnosis.

A Small Target Segmentation-Based Assistive Medical Diagnosis Method via Multimedia Data Analysis

The assistive medical diagnosis via multimedia data analysis has been a hot research topic in the area of intelligent health management, which saves a lot of human labor for the hospital. In that, a typical task is to detect and extract key small targets from the medical images. How to ensure detection speed and recognition precision is of great importance to the final practicability of the intelligent techniques. In this paper, we proposed a small target segmentation-based intelligent assistive diagnosis method for this purpose. In the stage of data pre-processing, data enhancement is employed to reduce the effects caused by noise, blur, and low image contrast. In the stage of decoding, a new upsampling method is designed to compensate for information in space and channels to help the network recover feature map resolution, reduce information loss, and improve the segmentation accuracy of smaller tissues or lesions. Such design of a multi-scale adaptive detail feature fusion module is able to make full use of features at different scales to recover high-level detail features, thus improving the segmentation accuracy of structurally complex tissues. Some simulative experiments aided by deep learning programming tools are conducted on the real-world multimedia data (CT, MRI, PET, etc.), so as to evaluate the proposed assistive diagnosis method. It can be concluded from the achieved results that the proposal is well suitable for multimedia data analysis of the experimental scenes.

Deep learning based detection of silicosis from computed tomography images

Artificial intelligence has increasingly been used in interpreting medical images to support the timely treatment of diseases by providing early and accurate diagnosis. Pneumoconiosis is a tissue reaction that develops as a result of the accumulation of inorganic dust in the lungs. The most common types of pneumoconiosis include diseases such as coal worker's pneumoconiosis, silicosis, asbestosis, and siderosis. Silicosis, which has maintained its importance since the 1900s and has seen over 182,000 articles published in the last 10 years, is a global health problem. The automated detection and recognition of silicosis in lung computed tomography (CT) images can be considered the backbone of assisting the silicosis diagnosis process. Automated medical assistance systems developed using artificial intelligence can simplify the medical examination process and reduce the time required to start accurate treatment. Although the literature contains various studies that benefit silicosis diagnosis using chest X-ray images or pneumoconiosis diagnosis using CT images, there is not enough classification study that can particularly aid the diagnosis of silicosis in CT images.The method of early detection of silicosis from chest radiographs and CT images has been a challenging task due to the high variability among pneumoconiosis readers. Based on the success of deep learning in the classification and segmentation of medical images, this study has shown that deep learning networks and transfer learning algorithms can detect silicosis with high accuracy by classifying CT images. The performance of the six algorithms examined in the study is compared, and the algorithm with the best performance is recommended. Performance criteria such as accuracy, precision, specificity, and F1-score of the algorithms used in the study were calculated. The accuracy rates of the models were obtained as 92.62 %, 93.03 %, 92.76 %, 95.38 %, 97.29 %, and 95.17 % for AlexNet, VGG16, ResNet50, InceptionV3, Xception, and DenseNet121, respectively. These results show that Xception outperformed the other algorithms and was the most successful algorithm in the automatic detection of silicosis with an accuracy rate of 97.29 %.Additionally, a new dataset consisting of tomography images from silicosis patients is presented in this study. Experimental results have shown that transfer learning algorithms can significantly benefit the diagnosis of silicosis by successfully classifying CT images. The findings of the study highlight the clinical importance of artificial intelligence methods in medical image analysis and early disease diagnosis.

Improved Demons algorithm for non-rigid medical image alignment

Abstract Medical image alignment is an important research field in medical image processing, which is widely used in clinical diagnosis and treatment, such as surgical navigation, lesion tracking, and treatment evaluation. In this paper, an improved algorithm combining the Demons algorithm and SIFT algorithm is proposed, which uses the SIFT algorithm to represent the feature points in non-rigid medical images as a scale space sequence and normalize the descriptors in the scale space sequence. Then, the two-way alignment strategy and multi-resolution strategy are introduced to improve the accuracy of Demons algorithm in the alignment of non-rigid medical images with complex deformation. The study shows that the improved Demons algorithm can achieve better alignment results when the weights of the feature matching terms are taken as −1 and 1, which makes the improved Demons algorithm with the addition of SIFT feature terms perform optimally. Alignment simulation experiments found that the MSE value of this paper’s improved algorithm is only 0.077. The alignment effect of non-rigid medical images is much better than the comparison algorithm and can maintain a shorter running time. The algorithm in this paper can effectively realize the non-rigid alignment of medical images, which provides a reference method for medical diagnosis and the effective formulation of treatment plans.

Hierarchical Hybrid Networks for Automatic Pulmonary Blood Vessel Segmentation in Computed Tomography Images.

Pulmonary arterial hypertension (PAH) is considered the third most common cardiovascular disease after coronary heart disease and hypertension. The diagnosis of PAH is mainly based on the comprehensive judgment of computed tomography and other medical image examinations. Medical image processing based on deep learning has achieved significant success. However, the data belongs to the patient's privacy; therefore, the medical institutions as data custodians have the responsibility to protect the security of their data privacy. This situation makes medical institutions face a dilemma when building data-driven deep learning-assisted medical diagnosis methods. On the one hand, they need to pursue more high-quality data based on Big Data architecture for deep learning; on the other hand, they need to protect patient privacy to avoid data leakage. In response to the above challenges, we propose a hierarchical hybrid automatic segmentation model for pulmonary blood vessels based on local learning and federated learning approaches for segmenting the pulmonary blood vessels. The experiments prove the proposal could automatically segment the vessels from the original CT. It also indicates that the model based on a federated learning approach can achieve impressive performance under the premise of protecting data privacy for Big Data.

Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis

(1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87–91), the specificity was 90% (95% CI: 87–92), and the AUC was 0.95 (95% CI: 0.93–0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field.

Extending statistical data of EEG biofeedback quality improvement with soft computing

Aim. Automatic processing of the data in order to determine the status of work and identification of the activity and brain-wave frequencies becomes necessary for the modern systems in the in the diagnosis of biofeedback among athletes.
 Concept. The study aimed to explore the effects of physical exertion on alterations in the manifestation of brain wave frequencies (pre/post exercises) in a group of 15 endurance athletes.
 Results and conclusion. Statistic methods allowed an identification of data anomalies, such as extreme, outliers and missing values. Combining information with soft computing tool can distinguish the level of electrical activity of the analysed muscles. Used Big Data and Data Mining tools solution with a statistical approach while maintaining high measurement accuracy indicates the effectiveness of this method in medical diagnosis.

Principles of Biomimetic solid-state nanopores and the application to biosensors

The wide application of biomimetic solid-state nanopores in biosensors has made them a high-profile research area. It can be applied in several fields such as genomics, proteomics, biomedicine, and environmental monitoring. Bionic solid-state nanopores have demonstrated the capability to detect biomolecules and creatures, including proteins, nucleic acids, cells, and microbes, with a notable degree of sensitivity and selectivity. Biomimetic solid-state nanopores offer several advantages over conventional biosensors. An innovative kind of biosensor is called biomimetic solid state nanopores. This study provides a comprehensive overview of the principle, construction, and use of the bionic solid state nanopore sensor. Bionic solid-state nanopores are of significant importance within the realm of biosensors. Firstly, it has micrometer or nanometer scale, which can effectively capture and control target molecules; Additionally, the dimensions of the biomimetic solid-state nanopores can be modified, along with the surface functionalization, in order to enhance the performance of the sensor and expand its potential applications. In addition, the structural stability and reusability of biomimetic solid-state nanopores also guarantee the long-term application of biosensors. Through in-depth research and development, it is expected to promote the technological progress in the field of biosensors, and provide more accurate and reliable detection methods for life science, medical diagnosis, environmental protection and other fields.

Explainable Deep Learning Methods in Medical Image Classification: A Survey

The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box nature of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation–based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are discussed.

A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis.

Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose, and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus.

Development of quality criteria for long-term testing of intracortical electrodes

Abstract The use of intracortical electrodes is a crucial method for medical diagnosis and treatment and holds the promise for overcoming various neural diseases and challenges in prosthetics. The general physical and electrical function as well as the biocompatibility is extensively tested before intracortical electrodes are implanted, however, their longterm performance, is still limited through tissue scarring and electrode deterioration, and consequently a decrease in signal quality. To overcome this challenge, aspects such as materials selection, coatings, flexibility, and surgical procedures are often addressed. To date, however, a systematic, industry - compatible process for long-term functional testing of new intracortical electrodes technologies is still lacking. Implementation of a general overall in vivo testing procedure with defined quality criteria would enable reliable in vivo longterm (> 30 days) device assessment. This could reduce the risk in developing probes for long-term performance and pave the way for clinical application in patients suffering from neural diseases.

Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging.

Computed tomography (CT) imaging technology has become an indispensable auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage caused by X-rays, low-dose computed tomography (LDCT) scanning is becoming more widely applied. However, LDCT scanning reduces the signal-to-noise ratio of the projection, and the resulting images suffer from serious streak artifacts and spot noise. In particular, the intensity of noise and artifacts varies significantly across different body parts under a single low-dose protocol. To improve the quality of different degraded LDCT images in a unified framework, we developed a generative adversarial learning framework with a dynamic controllable residual. First, the generator network consists of the basic subnetwork and the conditional subnetwork. Inspired by the dynamic control strategy, we designed the basic subnetwork to adopt a residual architecture, with the conditional subnetwork providing weights to control the residual intensity. Second, we chose the Visual Geometry Group Network-128 (VGG-128) as the discriminator to improve the noise artifact suppression and feature retention ability of the generator. Additionally, a hybrid loss function was specifically designed, including the mean square error (MSE) loss, structural similarity index metric (SSIM) loss, adversarial loss, and gradient penalty (GP) loss. The results obtained on two datasets show the competitive performance of the proposed framework, with a 3.22 dB peak signal-to-noise ratio (PSNR) margin, 0.03 SSIM margin, and 0.2 contrast-to-noise ratio margin on the Challenge data and a 1.0 dB PSNR margin and 0.01 SSIM margin on the real data. Experimental results demonstrated the competitive performance of the proposed method in terms of noise decrease, structural retention, and visual impression improvement.