Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.
Ontology highlight
ABSTRACT:
SUBMITTER: Ong W
PROVIDER: S-EPMC9406500 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Publications
Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.
Ong Wilson W Zhu Lei L Zhang Wenqiao W Kuah Tricia T Lim Desmond Shi Wei DSW Low Xi Zhen XZ Thian Yee Liang YL Teo Ee Chin EC Tan Jiong Hao JH Kumar Naresh N Vellayappan Balamurugan A BA Ooi Beng Chin BC Quek Swee Tian ST Makmur Andrew A Hallinan James Thomas Patrick Decourcy JTPD
Cancers 20220820 16
Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main e ...[more]
Similar Datasets
Project description:Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. The most important question when using multimodal data is how to fuse them-a field of growing interest among researchers. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. More specifically, we focus on studies that only fused EHR with medical imaging data to develop various AI methods for clinical applications. We present a comprehensive analysis of the various fusion strategies, the diseases and clinical outcomes for which multimodal fusion was used, the ML algorithms used to perform multimodal fusion for each clinical application, and the available multimodal medical datasets. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched Embase, PubMed, Scopus, and Google Scholar to retrieve relevant studies. After pre-processing and screening, we extracted data from 34 studies that fulfilled the inclusion criteria. We found that studies fusing imaging data with EHR are increasing and doubling from 2020 to 2021. In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies). We found that multimodality fusion models outperformed traditional single-modality models for the same task. Disease diagnosis and prediction were the most common clinical outcomes (reported in 20 and 10 studies, respectively) from a clinical outcome perspective. Neurological disorders were the dominant category (16 studies). From an AI perspective, conventional ML models were the most used (19 studies), followed by DL models (16 studies). Multimodal data used in the included studies were mostly from private repositories (21 studies). Through this scoping review, we offer new insights for researchers interested in knowing the current state of knowledge within this research field.
| S-EPMC9605975 | biostudies-literature
Project description:AI has, to varying degrees, affected all aspects of molecular imaging, from image acquisition to diagnosis. During the last decade, the advent of deep learning in particular has transformed medical image analysis. Although the majority of recent advances have resulted from neural-network models applied to image segmentation, a broad range of techniques has shown promise for image reconstruction, image synthesis, differential-diagnosis generation, and treatment guidance. Applications of AI for drug design indicate the way forward for using AI to facilitate molecular-probe design, which is still in its early stages. Deep-learning models have demonstrated increased efficiency and image quality for PET reconstruction from sinogram data. Generative adversarial networks (GANs), which are paired neural networks that are jointly trained to generate and classify images, have found applications in modality transformation, artifact reduction, and synthetic-PET-image generation. Some AI applications, based either partly or completely on neural-network approaches, have demonstrated superior differential-diagnosis generation relative to radiologists. However, AI models have a history of brittleness, and physicians and patients may not trust AI applications that cannot explain their reasoning. To date, the majority of molecular-imaging applications of AI have been confined to research projects, and are only beginning to find their ways into routine clinical workflows via commercialization and, in some cases, integration into scanner hardware. Evaluation of actual clinical products will yield more realistic assessments of AI's utility in molecular imaging.
| S-EPMC8246206 | biostudies-literature
Project description:We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (≤0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness.
2012-12-10 | GSE42804 | GEO
Project description:BackgroundComputed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer.MethodsWiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network.ResultsThe area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively.ConclusionsThe image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.
| S-EPMC9745522 | biostudies-literature
Project description:Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
| S-EPMC6465941 | biostudies-literature
Project description:We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (M-bM-^IM-$0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness. Female striped bass were sorted into groups (N=8 each) producing M-bM-^@M-^Xhigh qualityM-bM-^@M-^Y or M-bM-^@M-^Xlow qualityM-bM-^@M-^Y eggs (spawns) based upon the percentage of eggs bearing viable 4 h embryos. Spawns with >50% of eggs producing 4 h embryos were considered to be of high quality and spawns with <30% of eggs producing 4 h embryos were considered to be of low quality.
2012-12-10 | E-GEOD-42804 | biostudies-arrayexpress
Project description:Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.
| S-EPMC8500024 | biostudies-literature
Project description:Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
| S-EPMC6985036 | biostudies-literature
Project description:Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.
| S-EPMC10455092 | biostudies-literature
Project description:BackgroundTransformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation.ObjectiveThis study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging.MethodsThis review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach.ResultsOf the 736 retrieved studies, 22 (3%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer-based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data.ConclusionsIt can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer.
| S-EPMC10692876 | biostudies-literature