Diagnosis Of Systemic Diseases Research Articles

Explainable AI for Transparent and Trustworthy Tuberculosis Diagnosis: From Mere Pixels to Actionable Insights

Building transparent and trustworthy AI-powered systems for disease diagnosis has become more paramount than ever due to a lack of understanding of black box models. A lack of transparency and explainability in AI-driven models can propagate biases and erode patients' and medical practitioners' trust. To answer this challenge, Explainable AI (XAI) is drastically emerging as a practical solution and approach to tackling ethical concerns in the health sector. The overarching purpose of this paper is to highlight the advancement in XAI for tuberculosis diagnosis (TB) and identify the benefits and challenges associated with improved trust in AI-powered TB diagnosis. We explore the potential of XAI in improving TB diagnosis. We attempt to provide a complete plan to promote XAI. We examine the significant problems associated with using XAI in TB diagnosis. We argue that XAI is critical for reliable TB diagnosis by improving the interpretability of AI decision-making processes and recognising possible biases and mistakes. We evaluate techniques and methods for XAI in TB diagnosis and examine the ethical and societal ramifications. By leveraging explainable AI, we can create a more reliable and trustworthy TB diagnostic framework, ultimately improving patient outcomes and global health. Finally, we provide thorough recommendations for developing and implementing XAI in TB diagnosis using X-ray imaging

RFMiD: Retinal Image Analysis for multi-Disease Detection challenge

In the last decades, many publicly available large fundus image datasets have been collected for diabetic retinopathy, glaucoma, and age-related macular degeneration, and a few other frequent pathologies. These publicly available datasets were used to develop a computer-aided disease diagnosis system by training deep learning models to detect these frequent pathologies. One challenge limiting the adoption of a such system by the ophthalmologist is, computer-aided disease diagnosis system ignores sight-threatening rare pathologies such as central retinal artery occlusion or anterior ischemic optic neuropathy and others that ophthalmologists currently detect. Aiming to advance the state-of-the-art in automatic ocular disease classification of frequent diseases along with the rare pathologies, a grand challenge on “Retinal Image Analysis for multi-Disease Detection” was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2021). This paper, reports the challenge organization, dataset, top-performing participants solutions, evaluation measures, and results based on a new “Retinal Fundus Multi-disease Image Dataset” (RFMiD). There were two principal sub-challenges: disease screening (i.e. presence versus absence of pathology — a binary classification problem) and disease/pathology classification (a 28-class multi-label classification problem). It received a positive response from the scientific community with 74 submissions by individuals/teams that effectively entered in this challenge. The top-performing methodologies utilized a blend of data-preprocessing, data augmentation, pre-trained model, and model ensembling. This multi-disease (frequent and rare pathologies) detection will enable the development of generalizable models for screening the retina, unlike the previous efforts that focused on the detection of specific diseases.

A review of deep learning methods for gastrointestinal diseases classification applied in computer-aided diagnosis system.

Recent advancements in deep learning have significantly improved the intelligent classification of gastrointestinal (GI) diseases, particularly in aiding clinical diagnosis. This paper seeks to review a computer-aided diagnosis (CAD) system for GI diseases, aligning with the actual clinical diagnostic process. It offers a comprehensive survey of deep learning (DL) techniques tailored for classifying GI diseases, addressing challenges inherent in complex scenes, clinical constraints, and technical obstacles encountered in GI imaging. Firstly, the esophagus, stomach, small intestine, and large intestine were located to determine the organs where the lesions were located. Secondly, location detection and classification of a single disease are performed on the premise that the organ's location corresponding to the image is known. Finally, comprehensive classification for multiple diseases is carried out. The results of single and multi-classification are compared to achieve more accurate classification outcomes, and a more effective computer-aided diagnosis system for gastrointestinal diseases was further constructed.

A clinical expert system for Noma disease diagnosis

A clinical expert system for Noma disease diagnosis

EAR DISEASE CLASIFICATION USING DEEP LEARNING WITH XCEPTION AND MOBILENET-V2 ARCHITECTURE

Hearing loss is a significant global health problem, with a high prevalence in Indonesia. Limited access to ENT specialists, especially in remote areas, causes delays in diagnosis and treatment of ear diseases. This research aims to develop an early diagnosis system for ear diseases using deep learning. The proposed method applies Xception and MobileNet-V2 Convolutional Neural Network (CNN) architecture with hyperparameter optimization using Bayesian Optimizer. The dataset consists of 1,101 images covering 20 types of ear diseases, collected using an endoscope ear cleaning kit at Mataram University Hospital. The dataset was divided into 60% training data, 20% validation data, and 20% test data. Xception recorded the best performance with accuracy, precision, recall, and f1-score of 0.911, 0.166, 0.166, and 0.151, respectively. The best model performance was obtained on MobileNet-V2 with the application of Bayesian Optimizer, resulting in the best hyperparameters at Unit Dense 174, Dropout Rate 0.2, and LXceptionearning Rate 0.003. This scenario resulted in an increase in accuracy, precision, recall, and f1-score compared to the scenario without hyperparameter search of 0.004, 0.010, 0.018, and 0.012, respectively. This research demonstrates the potential of deep learning in improving early diagnosis of ear diseases.

A multi-modal Parkinson’s disease diagnosis system from EEG signals and online handwritten tasks using grey wolf optimization based deep learning model

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the gradual deterioration of motor function, affecting speech, writing, muscle control, and mobility. The existing studies have not utilized both the electroencephalography (EEG) signals and online handwritten tasks together to diagnose PD. The studies have also not explored the EEG signals collected from specific brain regions like the substansia niagra (SN) and ventral tegmental area (VTA), crucial for dopamine production linked to PD. This article proposes a multi-modal PD diagnosis system from EEG signals (collected from SN and VTA regions of the brain), collected during performing online handwritten tasks, using grey wolf optimization (GWO) algorithm. Mel-frequency cepstral coefficients (MFCC) features have been generated from the EEG signals and optimized by the GWO algorithm. The classification (diagnosis) experiments on the optimal number of feature values, obtained from GWO algorithm, have been carried out using bidirectional long short-term memory (BLSTM) variant of recurrent neural network (RNN). The classification experiments have also been conducted using support vector machine (SVM), bagged random forest (BRF), and long short-term memory (LSTM) variant of RNN classifier to have a performance comparison with the proposed method. The effectiveness of the introduced PD diagnosis system has been analyzed on a self-generated dataset named EEG signal based on online handwriting (ESOH). A maximum classification accuracy of 99.30% has been achieved from the proposed PD diagnosis system. The experimental outcomes illustrate that the introduced PD diagnosis system outperforms the state-of-the-art PD diagnosis systems relying on the EEG signals for diagnosing the PD.

Development of a Web-based Tomato Plant Disease Detection and Diagnosis System using Transfer Learning Techniques

A significant obstacle to agricultural productivity that jeopardizes the availability of food is crop diseases and farmer livelihoods by reducing crop yields. Traditional visual assessment methods for disease diagnosis are effective but complex, often requiring expert observers. Recent advancements in deep learning indicate the potential for increasing accuracy and automating disease identification. Developing accessible diagnostic tools, such as web applications leveraging CNNs, can provide farmers with efficient and accurate disease identification, especially in regions with limited access to advanced diagnostic technologies. The main goal is to develop a productive system that can recognize tomato plant diseases. The model was trained on a collection of images of healthy and damaged tomato leaves from PlantVillage using transfer learning techniques. The images from the dataset were cleansed by resizing them from 256 × 256 to 224 × 224 to match the dimensions used in pre-trained models using min-max normalization. An evaluation of VGG16, VGG19, and DenseNet121 models based on performance accuracy and loss value for 7 categories of tomatoes guided the selection of the most effective model for practical application. VGG16 achieved 84.54% accuracy, VGG19 achieved 84.62%, and DenseNet121 achieved 98.28%, making DenseNet121 the chosen model due to its highest performance accuracy. The web application development based on the DenseNet121 architecture was integrated using the Django web framework, which is built on Python. This enables real-time disease diagnosis for uploaded images of tomato leaves. The proposed system allows early detection and diagnosis of tomato plant diseases, helping to mitigate crop losses. This supports sustainable farming practices and increases agricultural productivity.

Development of an Expert System for Diagnosing Musculoskeletal Disease

Musculoskeletal diseases (MSDs), encompasses various conditions affecting muscles, bones, tendons, ligaments, and joints, resulting to pain, inflammation, and limited mobility, significantly impacting individuals' quality of life. Diagnosing these diseases poses a challenge for healthcare professionals due to symptom similarities with other conditions. To address this, the development of expert systems tailored for musculoskeletal diagnosis has emerged as a promising approach to enhance clinical decision-making and improve patient outcomes. This study aims at developing and evaluating an expert system for musculoskeletal disease diagnosis, by leveraging a knowledge base containing information on common musculoskeletal diseases and symptoms. The system utilized a combination of rule-based and machine learning techniques to provide diagnostic recommendations to physicians. Comparative analysis with experienced physicians, using a dataset of patients with known musculoskeletal diseases, revealed the expert system’s diagnostic accuracy of 92%, recall of 98%, Precision of 91%, F1-Score of 94% and a quicker diagnosis compared to physicians. Additionally, the system demonstrated ease of use and user-friendliness. This project focuses on predictive algorithms, leveraging expert systems dating back to the 1970s, emulating human expert decision-making, particularly in disease diagnosis. The development of an expert system for musculoskeletal disease diagnosis symbolizes the convergence of medical expertise, computer science, and artificial intelligence. By integrating machine learning, natural language processing, and decision support systems, these expert systems have the potential to revolutionize musculoskeletal healthcare delivery. In conclusion, our results show that expert systems hold promise in transforming clinical practice and improving patient outcomes in musculoskeletal healthcare through interdisciplinary collaboration and continuous innovation.

Salivary Short-chain Fatty Acids (SCFAs) Are Viable Predictors of Early Changes in Systemic Health.

Short-chain fatty acids (SCFAs) are the metabolites identified in both the oral cavity and the gut. They play an important role in the triggering, development and progression of systemic diseases. SCFAs can alter the gut microbial components, intestinal epithelium and host immune system, and are also associated with cancer incidence. Salivary SCFAs, produced by the oral microbiome, are correlated with some oral diseases. The occurrence of systemic diseases associated with gut SCFAs is more clearly defined than oral SCFAs. Salivary SCFAs can enter the bloodstream directly via inflamed gingiva to cause continuous low-grade systemic inflammation. Hence, salivary SCFAs could be an indicator for the early diagnosis of systemic diseases. Furthermore, they provide a basis for understanding the oral-systemic axis driven through salivary SCFAs in the pathogenesis of several diseases.

Advances in Oral Exfoliative Cytology: From Cancer Diagnosis to Systemic Disease Detection.

Oral exfoliative cytology has emerged as a valuable tool in the early detection of oral cancer and other systemic diseases. This review comprehensively examines the current applications and recent advancements in oral exfoliative cytology techniques. We analyzed published literature from the past decade, focusing on methodological improvements, diagnostic accuracy, and emerging applications. Key findings include: (1) Enhanced cell collection and preparation methods have significantly improved sample quality and diagnostic reliability. (2) Integration of molecular markers and DNA analysis with traditional cytomorphological assessment has increased diagnostic sensitivity and specificity for oral cancer detection. (3) Novel applications in systemic disease detection, including diabetes and iron overload disorders, demonstrate the expanding utility of this technique. (4) Computer-assisted analysis and deep learning algorithms show promise in improving diagnostic accuracy and efficiency. Despite these advancements, challenges remain in standardization and widespread clinical implementation. This review provides a critical evaluation of oral exfoliative cytology's current status and future potential in oral and systemic disease diagnosis.

Nailfold capillaroscopy for diagnosis of onychodystrophies: A prospective cross-sectional study

Nail diseases are often associated with significant physical and psychosocial burden, but diagnosis is challenging due to non-specific clinical and histological findings. Nail fold capillaroscopy has been studied for diagnosis of systemic diseases, but studies on nail diseases are lacking. Our objectives were to characterize and compare capillary changes in a set of nail conditions vs. controls, between nail groups, and based on demographic/clinical criteria. This was a prospective cross-sectional study of patients with nail psoriasis, onychomycosis, idiopathic onycholysis, brittle nail syndrome, nail lichen planus, retronychia, other nail conditions, and no nail findings (controls) undergoing capillaroscopy imaging/analysis. Nail psoriasis vs. control patients demonstrated decreased capillary length/density and increased abnormal capillaries, with higher frequency in older, male patients. Onychomycosis was associated with increased meandering capillaries compared to controls, nail psoriasis, and nail lichen planus. Retronychia is associated with increased disorganized polymorphic capillaries compared to controls and onychomycosis. Limitations include small sample size for certain nail conditions and small numbers of nail psoriasis patient with psoriatic arthritis. Our findings highlight nailfold capillaroscopy as a potentially quick, cost-effective, and non-invasive imaging modality, as an adjunct for diagnosis and treatment initiation for patients with onychodystrophies.

Sound Classification for Non-Destructive Diagnosis of Basal Stem Rot Disease based on Stem Density in Oil Palm Trunks

Oil palm trees play a crucial role in the economies of Southeast Asian countries, providing palm oil that is essential for various products and consumption. However, the Basal Stem Rot (BSR) disease caused by the white-rot fungus Ganoderma spp. poses a significant challenge, leading to wilting and death of the trees. This study proposes a practical and accessible approach for the routine detection of Basal Stem Rot (BSR) disease in oil palm trees caused by Ganoderma spp. A prototype system for initial disease diagnosis is developed by utilizing the local wisdom technique of tapping and assessing knocking sounds based on stem density in oil palm trunks. Three algorithms, Convolutional Neural Network (CNN), Support Vector Machine Classifier (SVC), and Multi-Layer Perceptron (MLP) models are compared for accuracy in two sound recognition experiments. The first experiment involves a three-class classification model (healthy, boundary, and infected areas), while the second excludes the boundary area. Results demonstrate that the CNN model outperforms SVC and MLP, achieving the highest accuracy of 90.73% in the two-class scenario and 84.97% in the three-class scenario, along with superior precision, recall, and F1 score. The CNN model proves to be the most effective in accurately classifying the data in both scenarios compared to the other models. This prototype system offers promising implications for the early detection and proactive management of Ganoderma spp. disease, contributing to the preservation and sustainability of oil palm plantations in Southeast Asia.

A Hybrid Approach for Thyroid Disease and Classification Using Attribute Associations Rule Mining

In recent years, thyroid diseases have become increasingly prevalent worldwide. In India, for instance, one in eight women is affected by hypothyroidism, hyperthyroidism, or thyroid cancer. Research indicates that approximately 20% of Indians are diagnosed with endemic goiter. Several factors influence thyroid function, including stress, infection, trauma, toxins, low-calorie diet, and certain medications. Preventing such diseases is crucial as treatments often involve long-term medication or surgical intervention. Thyroid disease diagnosis is pivotal in medicine, where precise classification is essential for prompt treatment. This paper proposes a novel approach to thyroid disease classification by integrating Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) with Association Rule Mining (ARM). The hybridization of PSO and ACO enhances exploration and exploitation capabilities, thereby improving the efficiency of association rule mining for feature selection. The proposed method aims to identify significant association rules among thyroid disease attributes, thereby facilitating accurate classification. Experimental evaluations conducted on benchmark thyroid disease datasets demonstrate the effectiveness of the proposed approach in terms of classification accuracy and computational efficiency. The results indicate that the hybrid PSO and ACO-based association rule mining approach surpasses traditional methods, underscoring its potential to enhance thyroid disease diagnosis systems.

Decision Support Systems for Disease Detection and Diagnosis

The last few years have been characterized by a large amount of research activity in the field of healthcare for both the improvement of diagnostic treatments and the development of simple, efficient, and multi-tasking applications [...]

Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models

Sugarcane is a crucial agricultural crop, providing 75% of the world's sugar production. Like all plant species, any disease that affects sugarcane can significantly impact yield and planning. Traditional manual methods for diagnosing diseases in sugarcane leaves are slow, inefficient, and often lack accuracy. In this study, we present a deep learning-based approach for the robust detection of diseases in sugarcane leaves. Specifically, we trained and evaluated all models from the EfficientNetv1 and EfficientNetv2 architectures, which are among the most notable convolutional neural network (CNN) architectures, using the publicly available Sugarcane Leaf Dataset. This dataset includes 11 disease classes and a total of 6748 images. Additionally, we compared these models with other popular CNN models. Our findings reveal that there is no direct correlation between model complexity, depth, and accuracy for the 11-class sugarcane dataset. Among the 13 models tested, EfficientNet-b6 and InceptionV4 achieved the highest accuracy rates of 93.39% and 93.10%, respectively. These results have a big impact on the ways managers can manage diseases and the agricultural processes of sugarcane production. A deep learning-based disease detection system facilitating the diagnostic process can, in turn, result in a more accurate and faster identification of diseases. That may enable farmers and agricultural managers to make timely and informed decisions, cutting down crop loss and enhancing the whole yield. These highlight the potential of deep learning in developing fast, accurate, and automatic disease diagnosis systems, which can significantly improve disease management and increase the yield of sugarcane.

Using in silico analysis to investigate the false positive potential of qPCR systems for potato disease diagnosis

Using in silico analysis to investigate the false positive potential of qPCR systems for potato disease diagnosis

The use of genetic algorithm and particle swarm optimization on tiered feature selection method in machine learning-based coronary heart disease diagnosis system

Coronary heart disease (CHD) is a leading global cause of death. Early detection is the right step to reduce mortality rates and treatment costs. Early detection can be developed using machine learning by utilizing patient medical record datasets. Unfortunately, this dataset has excessive features which can reduce machine learning performance. For this reason, it is necessary to reduce the number of redundant features and irrelevant data to improve machine learning performance. Therefore, this research proposes a tiered of feature selection model with genetic algorithm (GA) and particle swarm optimization (PSO) to improve the performance of the diagnosis model. The feature selection model is evaluated using parameters derived from the confusion matrix and using the CatBoost machine learning algorithm. Model testing uses z-Alizadeh Sani, Cleveland, Statlog, and Hungarian datasets. The best results for this model were obtained on the z-Alizadeh Sani dataset with 6 selected features from 54 features and the resulting performance for accuracy parameters was 99.32%, specificity 98.57%, sensitivity 100.00%, area under the curve (AUC) 99.28%, and F1-Score 99.37%. The proposed feature selection model is able to provide machine learning performance in the very good category. The diagnostic model proposed is of excellent standard.

A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model

In recent years, artificial intelligence has made remarkable strides, improving various aspects of our daily lives. One notable application is in intelligent chatbots that use deep learning models. These systems have shown tremendous promise in the medical sector, enhancing healthcare quality, treatment efficiency, and cost-effectiveness. However, their role in aiding disease diagnosis, particularly chronic conditions, remains underexplored. Addressing this issue, this study employs large language models from the GPT series, in conjunction with deep learning techniques, to design and develop a diagnostic system targeted at chronic diseases. Specifically, performed transfer learning and fine-tuning on the GPT-2 model, enabling it to assist in accurately diagnosing 24 common chronic diseases. To provide a user-friendly interface and seamless interactive experience, we further developed a dialog-based interface, naming it Chat Ella. This system can make precise predictions for chronic diseases based on the symptoms described by users. Experimental results indicate that our model achieved an accuracy rate of 97.50% on the validation set, and an area under the curve (AUC) value reaching 99.91%. Moreover, conducted user satisfaction tests, which revealed that 68.7% of participants approved of Chat Ella, while 45.3% of participants found the system made daily medical consultations more convenient. It can rapidly and accurately assess a patient’s condition based on the symptoms described and provide timely feedback, making it of significant value in the design of medical auxiliary products for household use.

Neuromuscular disease auxiliary diagnosis using a portable magnetomyographic system.

The measurement of electromyography (EMG) signals with needle electrodes is widely used in clinical settings for diagnosing neuromuscular diseases. Patients experience pain during needle EMG testing. It is significant to develop alternative diagnostic modalities. This paper proposes a portable magnetomyography (MMG) measurement system for neuromuscular disease auxiliary diagnosis. Firstly, the design and operating principle of the system are introduced. The feasibility of using the system for auxiliary diagnosis of neuromuscular diseases is then studied. The magnetic signals and needle EMG signals of thirty subjects were collected and compared. It is found that the amplitude of muscle magnetic field signal increases during mild muscle contraction, and the signal magnitudes of the patients are smaller than those of normal subjects. The diseased muscles tested in the experiment can be distinguished from the normal muscles based on the signal amplitude, using a threshold value of 6 pT. The MMG diagnosis results align well with the needle EMG diagnosis. In addition, the MMG measurement indicates that there is a persistence of spontaneous activity in the diseased muscle. The experimental results demonstrate that it is feasible to auxiliary diagnose neuromuscular diseases using the portable MMG system, which offers the advantages of non-contact and painless measurements. After more in-depth, systematic, and quantitative research, the portable MMG could potentially be used for auxiliary diagnosis of neuromuscular diseases.

Artificial Intelligence-Based System for Retinal Disease Diagnosis

Artificial Intelligence-Based System for Retinal Disease Diagnosis