Medical Diagnostic System Research Articles

Multifunctional Thermal Imaging Complex for Medical Diagnostics

Introduction. Recent developments in the field of thermal imaging technology and the accumulated experience in instrumental analysis of thermal processes in the patient’s body indicate the prospects of developing new medical thermal imaging systems (thermal imagers).Aim. Creation of a low-budget domestic thermal imaging system for medical diagnostics with expanded functionality.Materials and methods. The distribution of temperature over the surface of the human body depends on its internal state and external environment. For each person, this distribution has its own physiological characteristics, the study and interpretation of which can be significant for diagnosing specific pathologies or assessing the general (psychophysical) state of the patient. Analysis of temperature fields on the surface of the human body makes it possible to diagnose various pathological processes manifested in the form of local temperature changes in individual areas. The proposed method for thermal diagnostics involves measuring the temperature at each point of such an area simultaneously (statically) or over a period of time (dynamically). In comparison with conventional approaches, two thermal imaging sensors are used for this purpose. These include a small-sized matrix (thus being low cost) and a point sensor. Such a combination of sensors provides for the necessary discretization of the temperature field image of sufficiently large areas of the surface of the human body, when shooting from a distance significantly reduced compared to the conventional approach.Results. A conceptual electrical circuit and a layout of a modern thermal imaging system are developed. The advantages of the proposed thermal imager over similar devices are assessed. A method for recording and analyzing temperature in a certain point and temperature fields of both separate areas and large areas on the surface of the human body is proposed and tested.Conclusion. The conducted testing of a material model of the proposed thermal imaging system using the facilities of large medical institutions in St. Petersburg showed its wide functionality along with simplicity and convenience.

Моделювання процесу ранжування у нейромережному класифікаторі об’єктів

As part of expert systems for various purposes, one of the basic ones is the decision support subsystem, which, in turn, requires the need for a procedure for classifying ob-jects. This is especially evident in intelligent medical diagnostic systems, which widely use artificial intelligence methods and tools. In this context, an approach involving mod-ern neurotechnology methods has proven to be effective at a high level. This paper con-siders a variant of the structural organization of a neural network classifier of objects as an improved model of the Hamming neural network. The peculiarity of this variant of the classifier is the expansion of its functionality by forming the ranks of the classified ob-ject in all defined classes. In the case of medical diagnosis, this means ranking all possi-ble diagnoses of a disease, i.e. determining not only the most likely diagnosis, but also the closest in rank to it. In fact, this will allow us to clarify the diagnosis, and thus im-prove the results of medical diagnosis. Accordingly, we simulated the classification pro-cess with the ranking of results, which corresponds to the classification with the realiza-tion of competition between the neurons of the competitive layer using negative-reverse (lateral) connections. This approach is basic in the theory of neural networks for deter-mining the winning neuron according to the WTA (Winner Takes All). Simulation model-ing of the classification variant was performed using specific biomedical data (eight symptoms) for the diagnosis of appendicitis (four diagnoses). The results of modeling the processes of neural network classification of objects with the formation of appropri-ate ranks are presented in the form of a table. They confirmed the correctness of the functioning algorithm for the considered classification model.

Accelerating the Diagnosis of Pandemic Infection Based on Rapid Sampling Algorithm for Fast-Response Breath Gas Analyzers.

This paper presents a novel technique for extracting the alveolar part of human breath. Gas exchange occurs between blood and inhaled air in the alveoli, which is helpful in medical diagnostics based on breath analysis. Consequently, the alveolar portion of the exhaled air contains specific concentrations of endogenous EVOC (exogenous volatile organic compound), which, among other factors, depend on the person's health condition. As this part of the breath enables the screening for diseases, accurate sample collection for testing is crucial. Inaccurate sampling can significantly alter the composition of the specimen, alter the concentration of EVOC (biomarkers) and adversely affect the diagnosis. Furthermore, the volume of alveolar air is minimal (usually <350 mL), especially in the case of people affected by respiratory system problems. For these reasons, precise sampling is a key factor in the effectiveness of medical diagnostic systems. A new technique ensuring high accuracy and repeatability is presented in the article. It is based on analyzing the changes in carbon dioxide concentration in human breath using a fast and compensated non-dispersive infrared (NDIR) sensor and the simple moving adjacent average (SMAA) algorithm. Research has shown that this method accurately identifies exhalation phases with an uncertainty as low as 20 ms. This provides around 350 ms of breath duration for carrying out additional stages of the diagnostic process using various types of analyzers.

Research on the Design of Medical Imaging Assisted Diagnosis System Based on Machine Learning

The design and application of medical imaging diagnostic assistance systems have become an indispensable facet of modern medicine. Technologies based on machine learning can handle vast and intricate medical imaging data, offering precise diagnostic support and significantly enhancing the efficiency and accuracy of clinical diagnoses. This study focuses on the development and implementation of an efficient medical imaging diagnostic assistance system utilizing machine learning algorithms such as Principal Component Analysis (PCA), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP). Through the architectural design of intelligent diagnostic assistance systems, data preprocessing, and feature vectorization, combined with the application of classification algorithms, a robust diagnostic support system capable of addressing diverse medical scenarios has been constructed. Performance evaluation experiments indicate that this system demonstrates high accuracy and robustness in processing medical imaging data, holding potential for clinical implementation. The findings of this study provide new insights for the advancement of intelligent diagnostic systems in medical imaging and lay a solid foundation for the development of future clinical diagnostic tools.

Machine Learning Platform for Disease Diagnosis with Contrast CT Scans

Machine learning has gained significant recognition as a powerful approach for medical diagnosis using medical images. Among various medical imaging modalities, contrast-enhanced CT (CECT) is utilized to obtain additional diagnostic information that improves visualization and evaluation of certain abnormalities in the human body, as well as to observe temporal changes in lesions and tumors across different time phases. However, developing such medical diagnostic systems presents two significant challenges: high technical complexity and substantial development effort. This paper presents a software platform that effectively addresses these challenges. Specifically, we propose a unified software process that fully automates contrast-enhanced CT (CECT)-specific disease diagnosis, with key tasks performed by leveraging task-specific machine learning models to enhance accuracy. The platform incorporates a suite of specialized machine learning models into the diagnostic process, enabling precise diagnosis of lesions, malignancies, tumors, tumor characteristics, and temporal changes over phases. Moreover, the platform has been designed according to the Open–Closed Principle, allowing it to be applicable to a wide range of CECT-based diagnostic systems. The platform has been implemented in Python using the Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a hepatocellular carcinoma (HCC) diagnosis system has been implemented.

Analysis of the effectiveness of two different loss functions in training a neural network in lung image reconstruction using impedance tomography

The article presents research findings on developing a medical diagnostic system based on electrical impedance tomography technology. One of the key components of this project is developing a method for reconstructing the structure of human lungs using this technology. The authors of the article compared the effectiveness of two different loss functions in training a neural network, which is tasked with accurately replicating the lung structure based on electrical impedance tomography data. The researchers analyzed various approaches to calculating loss functions, including cosine embedding loss and InfoNCE loss. They compared the results obtained using these two functions to identify which one performs better in lung structure reconstruction. The findings of these studies may have significant implications for the development of diagnostic systems based on electrical impedance tomography and for improving the effectiveness of lung disease diagnosis. Additionally, the authors discuss potential future directions for the project, including possible applications of the research findings in clinical practice. Future research efforts may focus on optimizing neural network parameters, exploring alternative loss functions, or utilizing advanced machine learning techniques for even more precise lung structure reconstruction. The pursuit of improving such diagnostic systems could lead to significant advancements in the field of medicine, particularly in diagnosing and treating respiratory diseases.

Detection and analysis of disease entities based on lung conditions

The article presents a method for detecting and analysing disease entities associated with lung diseases. The results are related to work on the design of a medical diagnostic system based on impedance tomography. One of the key features of the solution is its ability to diagnose respiratory diseases, particularly chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS) and pneumothorax (PTX). The article describes the results of a classification model that effectively distinguishes between healthy and sick patients, achieving an impressive accuracy of 99.86%. This result underscores the robustness and reliability of the model. The conclusions of the presented research can serve as a basis for further work on improving diagnostic methods and introducing innovative healthcare solutions for patients with respiratory diseases, which may enable faster and more accurate diagnoses of lung diseases and provide more effective treatment and care for patients.

An Enhanced Adaptive Medical Diagnostic Model Driven By Genetic–Neuro-Fuzzy Algorithms

This research aims at developing adaptive medical diagnostic system driven by genetic, neural network and fuzzy logic algorithms for effective diagnosis of tuberculosis. Medical diagnosis for tuberculosis disease is a complex decision process that involves a lot of vagueness and uncertainty management, since the disease has multiple symptoms. The use of several algorithms has been explored in clinical diagnosis models for diagnosing and prescribing therapy for tuberculosis, but has challenges in transforming the vagueness and uncertainty of multiple symptoms due to the noisy nature of the disease data and over fitting of the models, this had led to the challenge of accurate classification, training, optimization, diagnosis and prescription of therapy. Object Oriented Analysis and Design (OOAD) and System Structured Analysis and Design (SSAD) methodologies were adopted in the research. The methodology involved obtaining four hundred and thirty (430) clinical data from patients with tuberculosis records at the Tuberculosis and Leprosy Hospital, Eku, Delta State. The obtained data were pre-processed using missing values imputation and numeric data encoding methods. Thereafter, a genetic algorithm was applied to the processed data to select six relevant features from the initial number of 16 features (Cough, Night sweats, Fever, Systolic Blood Pressure, Difficulty in Breathing, Loss of appetite, Sputum, Chills, Loss of pleasure, Immune Suppression, Chest Pain, Lack of concentration, Irritation, Loss of energy, Lymph Node Enlargement, Body Mass Index). The reduced dataset was split into training and test sets. The ANFIS (Adaptive Neuro-Fuzzy Inference System) was applied to perform diagnosis in which it was trained and validated on training and test sets respectively. The ANFIS was driven by Mamdani’s inference mechanism with sixty-four (64) generated rules with confidence and support scores of above 10% and 15% respectively. The developed model performance on the test sets gave an accuracy of 90% precision, sensitivity, and specificity of 100%. The results showed that all the attributes of tuberculosis contributed to the degree of the diseases based on their respective weights. Conclusively, the system accurately classified, trained attributes, and predicts the severity of tuberculosis.

Adaptive neighborhood triplet loss: enhanced segmentation of dermoscopy datasets by mining pixel information.

The integration of deep learning in image segmentation technology markedly improves the automation capabilities of medical diagnostic systems, reducing the dependence on the clinical expertise of medical professionals. However, the accuracy of image segmentation is still impacted by various interference factors encountered during image acquisition. To address this challenge, this paper proposes a loss function designed to mine specific pixel information which dynamically changes during training process. Based on the triplet concept, this dynamic change is leveraged to drive the predicted boundaries of images closer to the real boundaries. Extensive experiments on the PH2 and ISIC2017 dermoscopy datasets validate that our proposed loss function overcomes the limitations of traditional triplet loss methods in image segmentation applications. This loss function not only enhances Jaccard indices of neural networks by 2.42 and 2.21 for PH2 and ISIC2017, respectively, but also neural networks utilizing this loss function generally surpass those that do not in terms of segmentation performance. This work proposed a loss function that mined the information of specific pixels deeply without incurring additional training costs, significantly improving the automation of neural networks in image segmentation tasks. This loss function adapts to dermoscopic images of varying qualities and demonstrates higher effectiveness and robustness compared to other boundary loss functions, making it suitable for image segmentation tasks across various neural networks.

Carbon Micro/Nano Machining toward Miniaturized Device: Structural Engineering, Large-Scale Fabrication, and Performance Optimization.

With the rapid development of micro/nano machining, there is an elevated demand for high-performance microdevices withhigh reliability and low cost. Due to their outstanding electrochemical, optical, electrical, and mechanical performance, carbon materials are extensively utilized in constructing microdevices for energy storage, sensing, and optoelectronics. Carbon micro/nano machining is fundamental in carbon-based intelligent microelectronics, multifunctional integrated microsystems, high-reliability portable/wearable consumer electronics, and portable medical diagnostic systems. Despite numerous reviews on carbon materials, a comprehensive overview is lacking that systematically encapsulates the development of high-performance microdevices based on carbon micro/nano structures, from structural design to manufacturing strategies and specific applications. This review focuses on the latest progress in carbon micro/nano machining toward miniaturized device, including structural engineering, large-scale fabrication, and performance optimization. Especially, the review targets an in-depth evaluation of carbon-based micro energy storage devices, microsensors, microactuators, miniaturized photoresponsive and electromagnetic interference shielding devices. Moreover, it highlights the challenges and opportunities in the large-scale manufacturing of carbon-based microdevices, aiming to spark further exciting research directions and application prospectives.

Medical image intelligent diagnosis system based on facial emotion recognition and convolutional neural network

The facial emotional information of patients is one of the important indicators of their health status. By combining facial emotion recognition technology, medical imaging intelligent diagnostic systems can more comprehensively understand the psychological and physiological status of patients. This study first selected medical imaging datasets from the publicly available Medical Machine Learning Image Standard database and performed necessary preprocessing, including data cleaning, enhancement, and standardization. Next, a CNN model is constructed to extract key features from medical images and facial expression images through structures such as convolutional layers, activation functions, pooling layers, and fully connected layers. In addition, the system also integrates facial emotion recognition module and medical image analysis module, improving the comprehensiveness of diagnosis through feature fusion. Finally, the diagnostic results are displayed through a user interaction interface. Through comparative experiments, this study verified that the proposed CNN model outperforms traditional LSTM and SVM models in terms of accuracy, recall, and precision. The CNN model has shown significant advantages in medical imaging intelligent diagnosis systems, especially achieving a recall rate of 99%, demonstrating extremely high disease recognition ability. The results of this study not only provide new research directions for the field of intelligent diagnosis in medical imaging, but also lay a solid foundation for future clinical applications.

Medical image intelligent diagnosis system based on facial emotion recognition and convolutional neural network

The facial emotional information of patients is one of the important indicators of their health status. By combining facial emotion recognition technology, medical imaging intelligent diagnostic systems can more comprehensively understand the psychological and physiological status of patients. This study first selected medical imaging datasets from the publicly available Medical Machine Learning Image Standard database and performed necessary preprocessing, including data cleaning, enhancement, and standardization. Next, a CNN model is constructed to extract key features from medical images and facial expression images through structures such as convolutional layers, activation functions, pooling layers, and fully connected layers. In addition, the system also integrates facial emotion recognition module and medical image analysis module, improving the comprehensiveness of diagnosis through feature fusion. Finally, the diagnostic results are displayed through a user interaction interface. Through comparative experiments, this study verified that the proposed CNN model outperforms traditional LSTM and SVM models in terms of accuracy, recall, and precision. The CNN model has shown significant advantages in medical imaging intelligent diagnosis systems, especially achieving a recall rate of 99%, demonstrating extremely high disease recognition ability. The results of this study not only provide new research directions for the field of intelligent diagnosis in medical imaging, but also lay a solid foundation for future clinical applications.

Developing an explainable diagnosis system utilizing deep learning model: a case study of spontaneous pneumothorax

Objective. The trend in the medical field is towards intelligent detection-based medical diagnostic systems. However, these methods are often seen as ‘black boxes’ due to their lack of interpretability. This situation presents challenges in identifying reasons for misdiagnoses and improving accuracy, which leads to potential risks of misdiagnosis and delayed treatment. Therefore, how to enhance the interpretability of diagnostic models is crucial for improving patient outcomes and reducing treatment delays. So far, only limited researches exist on deep learning-based prediction of spontaneous pneumothorax, a pulmonary disease that affects lung ventilation and venous return. Approach. This study develops an integrated medical image analysis system using explainable deep learning model for image recognition and visualization to achieve an interpretable automatic diagnosis process. Main results. The system achieves an impressive 95.56% accuracy in pneumothorax classification, which emphasizes the significance of the blood vessel penetration defect in clinical judgment. Significance. This would lead to improve model trustworthiness, reduce uncertainty, and accurate diagnosis of various lung diseases, which results in better medical outcomes for patients and better utilization of medical resources. Future research can focus on implementing new deep learning models to detect and diagnose other lung diseases that can enhance the generalizability of this system.

A Noble Methodology for Designing Expert System on Cloud Environment: A Case Study of Medical Diagnostic System

A Noble Methodology for Designing Expert System on Cloud Environment: A Case Study of Medical Diagnostic System

Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time.

The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time. We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short-Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors. Risk factors highly correlated with COVID-19 are revealed. LSTM achieves the highest classification accuracy for disease severity prediction. Temporal Attention Long Short-Term Memory (TA-LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best permanence. The experimental results demonstrate the effectiveness of the proposed models. The proposed models can provide a computer-aided medical diagnostics system by simply using time series of serological indicators.

Health of Things Melanoma Detection System—detection and segmentation of melanoma in dermoscopic images applied to edge computing using deep learning and fine-tuning models

According to the World Health Organization (WHO), melanoma is a type of cancer that affects people globally in different parts of the human body, leading to deaths of thousands of people every year worldwide. Intelligent diagnostic tools through automatic detection in medical images are extremely effective in aiding medical diagnosis. Computer-aided diagnosis (CAD) systems are of utmost importance for image-based pre-diagnosis, and the use of artificial intelligence–based tools for monitoring, detection, and segmentation of the pathological region are increasingly used in integrated smart solutions within smart city systems through cloud data processing with the use of edge computing. This study proposes a new approach capable of integrating into computational monitoring and medical diagnostic assistance systems called Health of Things Melanoma Detection System (HTMDS). The method presents a deep learning–based approach using the YOLOv8 network for melanoma detection in dermatoscopic images. The study proposes a workflow through communication between the mobile device, which extracts captured images from the dermatoscopic device and uploads them to the cloud API, and a new approach using deep learning and different fine-tuning models for melanoma detection and segmentation of the region of interest, along with the cloud communication structure and comparison with methods found in the state of the art, addressing local processing. The new approach achieved satisfactory results with over 98% accuracy for detection and over 99% accuracy for skin cancer segmentation, surpassing various state-of-the-art works in different methods, such as manual, semi-automatic, and automatic approaches. The new approach demonstrates effective results in the performance of different intelligent automatic models with real-time processing, which can be used in affiliated institutions or offices in smart cities for population use and medical diagnosis purposes.

Artificial Intelligence and its Awareness and Utilization among Dental Students and Private Dental Practitioners at Alkharj, Saudi Arabia.

Artificial intelligence (AI) is commonly used in the modern day medical system for medical and dental imaging diagnostics, decision support, precision, hospital monitoring, robotic assistants, and so on. All branches of dentistry have a role of AI, like endodontics, cancer diagnosis, and cephalometric analysis. With the advancing technology, dental professionals need to upgrade themselves. To assess awareness and attitude of dental students and dental practitioners in Alkharj toward AI. A total of 100 dental students from a teaching institute and 100 private dental practitioners participated in the study. A closed-ended questionnaire was used containing 14 questions related to awareness and attitude toward AI. Participation was voluntary. 33% of study participants were aware of the working principle of AI; 68% of study subjects are aware of uses of AI in the dental field. 87% thinks AI helps in radiological diagnosis; 56.5% thinks AI helps in cancer detection. Awareness about AI among study participants was less than 50%. The overall attitude of dental professionals was positive.

Collaborative intelligent decision systems for safe and reliable AI-assisted medical image diagnostics

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.

Application and Development of Artificial Intelligence-based Medical Imaging Diagnostic Assistance System

Application and Development of Artificial Intelligence-based Medical Imaging Diagnostic Assistance System

The impact of digitalization on the quality and efficiency of information processing in medical institutions

Digitalization of the process of providing medical care in the practice of medical centers has an impact on the quality of services provided, forecasting the workload of medical personnel and diagnostic systems, as well as on the components of efficiency in the healthcare sector. The authors of this article consider the use of industry 4.0 tools in the field of healthcare. Wearable devices for collecting, transmitting and analyzing vital signs of patients in medical institutions were chosen as the object of research.