Computer-aided Decision Support Research Articles

A review of deep learning and Generative Adversarial Networks applications in medical image analysis

Nowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, identification of patterns, detection, etc. Deep learning models learn feature representations from images rather than handcrafted features. Hence, deep learning models are quickly becoming the state-of-the-art method to achieve good performances in different computer-aided decision-support systems in medical applications. Similarly, deep learning-based generative models called Generative Adversarial Networks (GANs) have recently been developed as a novel method to produce realistic-looking synthetic data. GANs are used in different domains, including medical imaging generation. The common problems, like class imbalance and a small dataset, in healthcare are well addressed by GANs, and it is a leading area of research. Segmentation, reconstruction, detection, denoising, registration, etc. are the important applications of GANs. So in this work, the successes of deep learning methods in segmentation, classification, cell structure and fracture detection, computer-aided identification, and GANs in synthetic medical image generation, segmentation, reconstruction, detection, denoising, and registration in recent times are reviewed. Lately, the review article concludes by raising research directions for DL models and GANs in medical applications.

Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images

IntroductionThe automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels. MethodsTo overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training. Results and discussionBased on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use.

PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

Colorectal cancer (CRC) is a malignant condition that affects the colon or rectum, and it is distinguished by abnormal cell growth in these areas. Colon polyps, which are abnormalities, can turn into cancer. To stop the spread of cancer, early polyp detection is essential. The timely removal of polyps without submitting a sample for histology is made possible by computer-assisted polyp classification. In addition to Locally Shared Features (LSF) and ensemble learning majority voting, this paper introduces a computer-aided decision support system named PolyDSS to assist endoscopists in segmenting and classifying various polyp classes using deep learning models like ResUNet and ResUNet++ and transfer learning models like EfficientNet. The PICCOLO dataset is used to train and test the PolyDSS model. To address the issue of class imbalance, data augmentation techniques were used on the dataset. To investigate the impact of each technique on the model, extensive experiments were conducted. While the classification module achieved the highest accuracy of 0.9425 by utilizing the strength of ensemble learning using majority voting, the proposed segmenting module achieved the highest Dice Similarity Coefficient (DSC) of 0.9244 using ResUNet++ and LSF. In conjunction with the Paris classification system, the PolyDSS model, with its significant results, can assist clinicians in identifying polyps early and choosing the best approach to treatment.

Application of Computer Aided Decision Support System Based on Data Mining in Public Management

Application of Computer Aided Decision Support System Based on Data Mining in Public Management

Classification of DBS microelectrode recordings using a residual neural network with attention in the temporal domain

During the Deep Brain Stimulation (DBS) surgery for Parkinson’s disease (PD), the main goal is to place the permanent stimulating electrode into an area of the brain that becomes pathologically hyperactive. This area, called Subthalamic Nucleus (STN), is small and located deep within the brain. Therefore, the main challenge is the precise localization of the STN region, considering various measurement errors and artifacts. In this paper, we have designed and developed a computer-aided decision support system for neurosurgical DBS surgery. The implementation of this system provides a novel method for calculating the expected position of the stimulating electrode based on the recordings of the electrical activity of brain tissue. The artificial neural network with attention is used to classify the microelectrode recordings and determine the final position of the stimulating electrode within the STN area. Experiments have verified the utility and efficiency of our system. The tests were carried out on many recordings collected during DBS surgeries, giving encouraging results. The experimental results demonstrate that deep learning methods extended with self-attention blocks compete with the other solutions. They provide significant robustness to recording artifacts and improve the accuracy of the stimulating electrode placement.

Best Practices for Artificial Intelligence and Machine Learning for Computer-Aided Diagnosis in Medical Imaging

The American Association of Physicists in Medicine (AAPM) Task Group (TG) 273 has been charged with developing recommendations on best practices for the development and performance assessment of computer-aided decision support systems. The TG report [1] addresses broad issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal was to bring attention to issues such as proper data collection and training and validation methods for ML algorithms, aiming to improve generalizability and reliability and thus accelerate the adoption of CAD-AI systems for clinical decision support. The report focuses on several developmental stages of CAD-AI: data collection, reference standards, model development, performance assessment, and translation to the clinic.

Real-Time Evaluation of Helicobacter pylori Infection by Convolution Neural Network During White-Light Endoscopy: A Prospective, Multicenter Study (With Video).

Convolutional neural network during endoscopy may facilitate evaluation of Helicobacter pylori infection without obtaining gastric biopsies. The aim of the study was to evaluate the diagnosis accuracy of a computer-aided decision support system for H. pylori infection (CADSS-HP) based on convolutional neural network under white-light endoscopy. Archived video recordings of upper endoscopy with white-light examinations performed at Sir Run Run Shaw Hospital (January 2019-September 2020) were used to develop CADSS-HP. Patients receiving endoscopy were prospectively enrolled (August 2021-August 2022) from 3 centers to calculate the diagnostic property. Accuracy of CADSS-HP for H. pylori infection was also compared with endoscopic impression, urea breath test (URT), and histopathology. H. pylori infection was defined by positive test on histopathology and/or URT. Video recordings of 599 patients who received endoscopy were used to develop CADSS-HP. Subsequently, 456 patients participated in the prospective evaluation including 189 (41.4%) with H. pylori infection. With a threshold of 0.5, CADSS-HP achieved an area under the curve of 0.95 (95% confidence interval [CI], 0.93-0.97) with sensitivity and specificity of 91.5% (95% CI 86.4%-94.9%) and 88.8% (95% CI 84.2%-92.2%), respectively. CADSS-HP demonstrated higher sensitivity (91.5% vs 78.3%; mean difference = 13.2%, 95% CI 5.7%-20.7%) and accuracy (89.9% vs 83.8%, mean difference = 6.1%, 95% CI 1.6%-10.7%) compared with endoscopic diagnosis by endoscopists. Sensitivity of CADSS-HP in diagnosing H. pylori was comparable with URT (91.5% vs 95.2%; mean difference = 3.7%, 95% CI -1.8% to 9.4%), better than histopathology (91.5% vs 82.0%; mean difference = 9.5%, 95% CI 2.3%-16.8%). CADSS-HP achieved high sensitivity in the diagnosis of H. pylori infection in the real-time test, outperforming endoscopic diagnosis by endoscopists and comparable with URT. Clinicaltrials.gov ; ChiCTR2000030724.

Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study

Particle Swarm Optimization (PSO) is a classic and popularly used meta-heuristic algorithm in many real-life optimization problems due to its less computational complexity and simplicity. The binary version of PSO, known as BPSO, is used to solve binary optimization problems, such as feature selection. Like other meta-heuristic optimization techniques designed on the continuous search space, PSO uses the transfer functions (TFs) to map the candidate solutions to the discrete search space in BPSO, and these TFs play a vital role to get the desired results. Over the years, many forms of TFs have been introduced in the literature, most of which fall under one of the five families - Linear, S-shaped, V-shaped, U-shaped, and Time-varying Mirrored S-shaped TFs. The goal of this study is to determine an appropriate setup constituting a TF and a classifier for feature selection from different types of clinical data. In this study, the impacts of the five TF families have been investigated, considering one from each family for the selection of attributes/features, while predicting disease using diagnosis or medical reports. The classification tasks are carried out using four standard classifiers: Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Gaussian Naive Bayes. For experimental purposes, we have used four publicly available datasets namely, the UCI Heart Disease dataset, Wisconsin Breast Cancer dataset, UCI Chronic Kidney Disease dataset, and PIMA Indians Diabetes dataset. After an exhaustive set of experiments, we have obtained 96.72%, 99.82%, 100.00%, and 84.41% disease prediction scores in the best case for Heart disease, Breast Cancer, Chronic Kidney disease, and Diabetes, respectively. The obtained results are comparable to several state-of-the-art methods considered here for comparison. The present study helps in selecting a suitable BPSO setup (i.e., a TF and a classifier) to select important diagnostic attributes useful to design a computer-aided decision support system for the said diseases.

Development of a computer aided decision support system for the design of drip irrigation laterals

Development of a computer aided decision support system for the design of drip irrigation laterals

Review on Cleveland Heart Disease Dataset using Machine Learning

According to the World Health Organization (WHO), heart disease has been a foremost source of death worldwide for the past 15 years. Therefore Medical diagnosis is typically performed mostly by doctors due to their training and experience. In the field of medicine, computer-aided decision support systems are enormously significant. Therefore, it is necessary to develop prediction systems that give information of different categories to readers. According to the study, hybrid intelligent algorithms increase the heart disease prediction system’s accuracy. Hence, recognizing cardiovascular problems including heart attacks, coronary artery diseases, etc. by routine clinical data analysis is an important task; early identification of heart disease may save many lives. In this article, have reviewed various papers related to the Cleveland heart disease dataset that used one or more machine-learning algorithms to forecast congestive heart failure. In one of the above mention papers, the result of utilizing Random Forest is almost 100%. To ensure that predictions made using machine learning algorithms produce accurate outcomes. Applying machine learning algorithms to heart disease treatment data can produce results that are just as accurate as those found in heart disease diagnosis.

Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support for Facial Rehabilitation.

Backgrounds and Objective: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods: 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results: The skull prediction's median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions: The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games.

Computer-aided decision support and 3D modelling in pancreatic cancer surgery

Computer-aided decision support and 3D modelling in pancreatic cancer surgery

AAPM task group report 273: Recommendations on best practices for ai and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both 'traditional' machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support. This article is protected by copyright. All rights reserved.

Improving automated thyroid cancer classification of frozen sections by the aid of virtual image translation and stain normalization

Frozen sections are rapidly generated during surgical interventions. This allows surgeons to wait for histological findings during the interventions in order to base intra-surgical decisions on the outcome of the histology. However, compared to paraffin sections the quality of frozen sections is often strongly reduced, leading to a lower diagnostic accuracy. Deep learning-based image translation technology facilitates a virtual conversion between different native imaging technologies with the potential of translating a frozen section into a virtual paraffin section. Stain normalization can be applied to adjust further unequal image characteristics. We investigated the effect of deep learning-based image translation, conventional image normalization and a combination of these techniques on computer aided decision support systems for thyroid cancer diagnostics. For classification, a bag-of-words approach, based on convolutional neural network features, k-means clustering and a support vector machine were employed. While stain normalization led to a decreased overall classification accuracy (0.703 vs 0.727), image translation led to an increased mean score (0.770). A combination of both, image translation and normalization increased the accuracy even further (0.844) and clearly reduced the gap to the post-operative paraffin sections (0.902). Deep learning-based image translation proved to be a powerful tool to enhance accuracy of computer aided diagnosis which clearly outperformed conventional stain translation. This work provides a strong motivation for performing a study with expert pathologists performing the categorization of frozen sections and the corresponding improved sections, to investigate whether a similar effect is achieved in a clinical setting.

CRITICALITY OF SUSTAINABLE DISASTER MANAGEMENT THROUGH COMMUNICATION, SITUATIONAL AWARENESS, AND RESOURCE MANAGEMENT

The sustainability development process is crucial towards the management of disasters. This study describes the criticality of communication, situational awareness and resource management as part of an effective disaster management process during response and early recovery in minimizing the impact of disasters and their losses. Being part of a larger research, the study utilizes a validated and published checklist to observe lead responding agencies and is validated via three validation approaches involving exercise logs by the exercise controller, lead agency officers at the site, and final emergency exercise reports. Results are also reinforced with a survey questionnaire towards lead responding agency decision-makers during disasters. Both methods allow the researcher to differentiate between the respondent's real-time response and the intended outcome. It is found that the enhancement of institutional capacities of nations' emergency responders can significantly improve and ensure sustainability in managing disasters through effective emergency management elements of communication, situational awareness and resource management. Hence, it is agreed that emergency responders should adopt a system-thinking approach and computer-aided decision support system for the management of emergencies and disasters. Keywords: Emergency Responder, Disaster Management, Situational Awareness, Resource Management, Communication Management DOI： https://doi.org/10.35741/issn.0258-2724.58.2.70

Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients.

The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent development of deep learning (DL) models opens new challenges for 3D shape reconstruction from a single image. However, the 3D face shape reconstruction of facial palsy patients is still a challenge, and this has not been investigated. The contribution of the present study is to apply these state-of-the-art methods to reconstruct the 3D face shape models of facial palsy patients in natural and mimic postures from one single image. Three different methods (3D Basel Morphable model and two 3D Deep Pre-trained models) were applied to the dataset of two healthy subjects and two facial palsy patients. The reconstructed outcomes were compared to the 3D shapes reconstructed using Kinect-driven and MRI-based information. As a result, the best mean error of the reconstructed face according to the Kinect-driven reconstructed shape is 1.5±1.1 mm. The best error range is 1.9±1.4 mm when compared to the MRI-based shapes. Before using the procedure to reconstruct the 3D faces of patients with facial palsy or other facial disorders, several ideas for increasing the accuracy of the reconstruction can be discussed based on the results. This present study opens new avenues for the fast reconstruction of the 3D face shapes of facial palsy patients from a single image. As perspectives, the best DL method will be implemented into our computer-aided decision support system for facial disorders.

Çok Ölçekli Temel Bileşen Analizi ve Evrişimsel Sinir Ağları ile Elektrokardiyografi Sinyallerinden Miyokart Enfarktüsünün Tespiti

Myocardial Infarction is a vital disease that needs to be intervened in a very short time. The analysis of the patient's electrocardiography (ECG) data has an important place in the diagnosis. For this reason, computer aided decision support systems have been used in recent years in order to determine this disease more quickly and accurately. In this study, classification was made using convolutional neural network algorithms on the ECG signals obtained from 61 patients diagnosed with myocardial infarction and 52 healthy individuals. ECG signals are preprocessed with three different filters by applying finite impulse response (FIR) filter, infinite impulse response (IIR) filter and multiscale principal component analysis. According to the results obtained, classification success was achieved with 92.3% accuracy by using the preprocessed signals using multi-scale principal component analysis, and it was seen that more successful classification performance was obtained compared to the classification of the preprocessed signals with the help of FIR, IIR filter.

Comparison of classification techniques in the diagnosis of acute myelogenous leukemia

The leukemia blood cell discriminates from other blood cells by geometrical structure and cellular material by hematologist with microscope. In conventional color imaging system, the granular region in leukemia cells classify false negatively by experts due to micro geometrical and morphological changes in leukemia cell. Hence, a computer aided decision support system (CADSS) employs for automatic classification of leukemia blood cells. This paper gives the comparative study of various methods of classification of Acute Myelogenous Leukemia (AML) with improved accuracy and their results are compared. In this paper, the classification techniques such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Fraction Black-Widow Optimized Neural Network (FB-NN), Deep CNN with Arithmetic Optimization and Hybrid Convolutional Bi-LSTM based RNN are made with blood microscopic images, in which according to measures, Deep CNN with Arithmetic Optimization has the best accuracy.

Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning.

Segmentation of a liver in computed tomography (CT) images is an important step toward quantitative biomarkers for a computer-aided decision support system and precise medical diagnosis. To overcome the difficulties that come across the liver segmentation that are affected by fuzzy boundaries, stacked autoencoder (SAE) is applied to learn the most discriminative features of the liver among other tissues in abdominal images. In this paper, we propose a patch-based deep learning method for the segmentation of a liver from CT images using SAE. Unlike the traditional machine learning methods, instead of anticipating pixel by pixel learning, our algorithm utilizes the patches to learn the representations and identify the liver area. We preprocessed the whole dataset to get the enhanced images and converted each image into many overlapping patches. These patches are given as input to SAE for unsupervised feature learning. Finally, the learned features with labels of the images are fine tuned, and the classification is performed to develop the probability map in a supervised way. Experimental results demonstrate that our proposed algorithm shows satisfactory results on test images. Our method achieved a 96.47% dice similarity coefficient (DSC), which is better than other methods in the same domain.

Computer-Aided Decision Support System for Diagnosis of Heart Diseases

Computer-Aided Decision Support System for Diagnosis of Heart Diseases