Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study.

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies.

Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.

AI-based classification algorithms in SPECT myocardial perfusion imaging for cardiovascular diagnosis: a review.

In the last few years, deep learning has made a breakthrough and established its position in machine learning classification problems in medical image analysis. Deep learning has recently displayed remarkable applicability in a range of different medical applications, as well as in nuclear cardiology. This paper implements a literature review protocol and reports the latest advances in artificial intelligence (AI)-based classification in SPECT myocardial perfusion imaging in heart disease diagnosis. The representative and most recent works are reported to demonstrate the use of AI and deep learning technologies in medical image analysis in nuclear cardiology for cardiovascular diagnosis. This review also analyses the primary outcomes of the presented research studies and suggests future directions focusing on the explainability of the deployed deep-learning systems in clinical practice.

Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities.

Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods for their efficiency in revealing and suggesting potential image biomarkers. Eleven biomedical image datasets of various modalities are utilised, including SPECT, CT, photographs, microscopy, and X-ray. Seven state-of-the-art CNNs are employed and tuned to perform image classification in tasks. The main conclusion of the research is that DL reveals potential biomarkers in several cases, especially when the models are trained from scratch in domains where low-level features such as shapes and edges are not enough to make decisions. Furthermore, in some cases, device acquisition variations slightly affect the performance of DL models.

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images.

(1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks have proven to be very beneficial in achieving near-optimal accuracy for the automatic classification of SPECT images. (2) Methods: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD. For comparison purposes, we employ VGG-16 and DenseNet-121 pre-trained networks that are indulged in an image dataset representing stress and rest mode heart states acquired by SPECT. In experimentally evaluating the method, we explore a wide repertoire of deep learning network setups in conjunction with various robust evaluation and exploitation metrics. Additionally, to overcome the image dataset cardinality restrictions, we take advantage of the data augmentation technique expanding the set into an adequate number. Further evaluation of the model was performed via 10-fold cross-validation to ensure our model's reliability. (3) Results: The proposed RGB-CNN model achieved an accuracy of 91.86%, while VGG-16 and DenseNet-121 reached 88.54% and 86.11%, respectively. (4) Conclusions: The abovementioned experiments verify that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making.

Deep learning exploration for SPECT MPI polar map images classification in coronary artery disease.

OBJECTIVE: The exploration and the implementation of a deep learning method using a state-of-the-art convolutional neural network for the classification of polar maps represent myocardial perfusion for the detection of coronary artery disease. SUBJECTS AND METHODS: In the proposed research, the dataset includes stress and rest polar maps in attenuation-corrected (AC) and non-corrected (NAC) format, counting specifically 144 normal and 170 pathological cases. Due to the small number of the dataset, the following methods were implemented: First, transfer learning was conducted using VGG16, which is applied broadly in medical industry. Furthermore, data augmentation was utilized, wherein the images are rotated and flipped for expanding the dataset. Secondly, we evaluated a custom convolutional neural network called RGB CNN, which utilizes fewer parameters and is more lightweight. In addition, we utilized the k-fold validation for evaluating variability and overall performance of the examined model. RESULTS: Our RGB CNN model achieved an agreement rating of 92.07% with a loss of 0.2519. The transfer learning technique (VGG16) attained 95.83% accuracy. CONCLUSIONS: The proposed model could be an effective tool for medical classification problems, in the case of polar map data acquired from myocardial perfusion images.

Technetium-99m depreotide imaging by single photon emission tomography/low resolution computed tomography in malignant lymphomas: comparison with gallium-67 citrate.

OBJECTIVE: Previous studies have demonstrated the feasibility of targeting lymphoma lesions with somatostatin receptor binding agents, mainly with In-111-pentetreotide. In the present work another somatostatin analog, Tc-99m depreotide, is investigated. METHODS: One-hundred and six patients, 47 with Hodgkin's (HL) and 59 with various types of non-Hodgkin's lymphoma (NHL), were imaged with both Tc-99m depreotide and Ga-67 citrate. Planar whole-body and single photon emission tomography/low resolution computerized tomography (SPECT/CT) images were obtained. A total of 142 examinations were undertaken at different phases of the disease. Depreotide and gallium findings were compared visually and semi-quantitatively, with reference to the results of conventional work-up and the patients' follow-up data. RESULTS: In most HL, intermediate- and low-grade B-cell, as well as in T-cell NHL, depreotide depicted more lesions than Ga-67 and/or exhibited higher tumor uptake. The opposite was true in aggressive B-cell NHL. However, there were notable exceptions in all lymphoma subtypes. During initial staging, 93.3% of affected lymph nodes above the diaphragm, 100% of inguinal nodes and all cases with splenic infiltration were detected by depreotide. On the basis of depreotide findings, 32% of patients with early-stage HL were upstaged. However, advanced HL and NHL cases were frequently downstaged, due to low sensitivity for abdominal lymph node (22.7%), liver (45.5%) and bone marrow involvement (36.4%). Post-therapy, depreotide detected 94.7% of cases with refractory disease or recurrence. Its overall specificity was moderate (57.1%). Rebound thymic hyperplasia, various inflammatory processes and sites of unspecific uptake were the commonest causes of false positive findings. The combination of depreotide and gallium enhanced sensitivity (100%), while various false positive results of either agent could be avoided. CONCLUSION: Except perhaps for early-stage HL, Tc-99m depreotide as a stand-alone imaging modality has limited value for the initial staging of lymphomas. Post-therapy, however, depreotide scintigraphy seems useful in the evaluation of certain anatomic areas, particularly in non-aggressive lymphoma types. The combination with Ga-67 potentially enhances sensitivity and specificity. If fluorodeoxyglucose positron emission tomography is not available or in case of certain indolent lymphoma types, Tc-99m depreotide may have a role as an adjunct to conventional imaging procedures.