Gaussian process classification of superparamagnetic relaxometry data: Phantom study

Sovizi, Javad; Mathieu, Kelsey Boitnott; Thrower, Sara L.; Stefan, Wolfgang; Hazle, John D.; Fuentes, David

doi:10.1016/j.artmed.2017.07.001

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…It can easily handle a variety of problems, such as an insufficient capacity for the classical linear method, complex data types, and the curse of dimensions (28). Sovizi et al reported that they achieved high sensitivity results using GP classification model (29). Consistent with their results, the AUCs of different datasets by GP algorithm were greater than approximately 0.900 with the RFE/Relief feature selection methods.…”

Section: Discussionmentioning

confidence: 70%

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Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors

et al. 2021

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ObjectiveWe used texture analysis and machine learning (ML) to classify small round cell malignant tumors (SRCMTs) and Non-SRCMTs of nasal and paranasal sinus on fat-suppressed T2 weighted imaging (Fs-T2WI).MaterialsPreoperative MRI scans of 164 patients from 1 January 2018 to 1 January 2021 diagnosed with SRCMTs and Non-SRCMTs were included in this study. A total of 271 features were extracted from each regions of interest. Datasets were randomly divided into two sets, including a training set (∼70%) and a test set (∼30%). The Pearson correlation coefficient (PCC) and principal component analysis (PCA) methods were performed to reduce dimensions, and the Analysis of Variance (ANOVA), Kruskal-Wallis (KW), and Recursive Feature Elimination (RFE) and Relief were performed for feature selections. Classifications were performed using 10 ML classifiers. Results were evaluated using a leave one out cross-validation analysis.ResultsWe compared the AUC of all pipelines on the validation dataset with FeAture Explorer (FAE) software. The pipeline using a PCC dimension reduction, relief feature selection, and gaussian process (GP) classifier yielded the highest area under the curve (AUC) using 15 features. When the “one-standard error” rule was used, FAE also produced a simpler model with 13 features, including S(5,-5)SumAverg, S(3,0)InvDfMom, Skewness, WavEnHL_s-3, Horzl_GlevNonU, Horzl_RLNonUni, 135dr_GlevNonU, WavEnLL_s-3, Teta4, Teta2, S(5,5)DifVarnc, Perc.01%, and WavEnLH_s-2. The AUCs of the training/validation/test datasets were 1.000/0.965/0.979, and the accuracies, sensitivities, and specificities were 0.890, 0.880, and 0.920, respectively. The best algorithm was GP whose AUCs of the training/validation/test datasets by the two-dimensional reduction methods and four feature selection methods were greater than approximately 0.800. Especially, the AUCs of different datasets were greater than approximately 0.900 using the PCC, RFE/Relief, and GP algorithms.ConclusionsWe demonstrated the feasibility of combining artificial intelligence and the radiomics from Fs-T2WI to differentially diagnose SRCMTs and Non-SRCMTs. This non-invasive approach could be very promising in clinical oncology.

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Section: Discussionmentioning

confidence: 70%

“…Sovizi et al. reported that they achieved high sensitivity results using GP classification model ( 29 ). Consistent with their results, the AUCs of different datasets by GP algorithm were greater than approximately 0.900 with the RFE/Relief feature selection methods.…”

Section: Discussionmentioning

confidence: 99%

Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors

et al. 2021

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“…Beyond the realms of preoperative risk assessment and monitoring, the expansive potential of AI becomes evident in its ability to harmonize and dissect past patient data (e.g., anesthesia or procedural sedation history) alongside intricate pathophysiological insights, thereby heralding a new era of personalized anesthesia management, inclusive of perioperative pain management leading to active rehabilitation [ 32 , 33 ]. In other words, there is a possibility of further ERAS improvement.…”

Section: Reviewmentioning

confidence: 99%

Revitalizing Postoperative Pain Management in Enhanced Recovery After Surgery via Inter-departmental Collaboration Toward Precision Medicine: A Narrative Review

Komasawa

2024

Cureus

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This narrative review explores the crucial aspects of postoperative pain management within the framework of Enhanced Recovery After Surgery (ERAS). It emphasizes the significance of effective and secure pain management, highlighting its impact on patient well-being, surgical outcomes, and hospital stays. The inadequacy of perioperative pain relief increases the risk of persistent postoperative pain, emphasizing the need to challenge the notion that pain is expected after surgery. The goals of postoperative pain management extend beyond mere relief, encompassing comfortable sleep, pain-free rest, and liberation from pain during initial recovery. Inadequate pain management can lead to complications such as heightened postoperative bleeding and an increased risk of thrombosis. The review delves into various analgesic methods, their complications, and safety measures. ERAS programs, focused on reducing complications and medical costs, emphasize the importance of judicious postoperative pain management and active rehabilitation. The review discusses complications associated with analgesic methods like opioids, epidural analgesia, and adjuvant analgesics. Collaboration within the perioperative management team is crucial for effective postoperative pain relief. Interdepartmental collaboration is essential for evaluating surgical procedures, analgesic methods, and crisis management strategies. The review concludes by integrating precision medicine into postoperative pain management, emphasizing the potential of genetic information in assessing pain sensitivity. It underscores the importance of inter-departmental collaboration and information gathering for the successful implementation of precision medicine tailored to each facility's perioperative management systems. Additionally, the impact of artificial intelligence (AI) on preoperative risk assessment and innovative monitoring techniques is discussed, paving the way for the advancement of precision medicine in postoperative pain management.

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“…One type of problem addressed by the CI is the pattern classification problem, such as text recognition [29] [30], image recognition [31], classification of bone fractures [32] [33], endometriosis [34], arrhythmia [35] [36], mineral quality [37] and the identification of medicinal herbs [38], to name a few. Among the many techniques available to address classification problems, we may cite Naïve Bayes [29] [39], Decision Trees [40] [41] [42], Support Vector Machines [42] [43], Gaussian Process Classification [44] [45] [46], k-Nearest Neighbors [47]…”

Section: The Field Of CI Involves Paradigms Of Computational Science and Operationalmentioning

confidence: 99%

Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

Franco¹,

Steiner²

2021

JGIS

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The increasing demand for energy has intensified recently, requiring alternative sources to fossil fuels, which have become economically and environmentally unfeasible. On the other hand, the increasing land occupation in recent centuries is a growing problem, demanding greater efficiency, particularly in the reuse of abandoned areas, which has become an alternative. An interesting alternative would be installing energy facilities like solar, wind, biomass, and geothermal, in these areas. The objective of this paper is to develop a classification methodology, based on Artificial Intelligence (AI) and Quantum Theory (QT), to automatically carry out the classification of abandoned areas suitable for the settlement of these power plants. Artificial Neural Networks (ANNs) improved by the hybrid algorithm Quantum-behaved Particle Swarm Optimization (QPSO) together with the Levenberg-Marquardt Algorithm (LMA) were used for the classification task. In terms of Mean Squared Error (MSE), the QPSO-LMA approach achieved a decrease of 19.6% in relation to the classical LMA training with random initial weights. Moreover, the model's accuracy showed an increase of 7.3% for the QPSO-LMA over the LMA. To validate this new approach, it was also tested on six different datasets available in the UCI Machine Learning Repository and seven classical techniques established in the literature. For the problem of installing photovoltaic plants in abandoned areas, the knowledge acquired with the solar dataset can be extrapolated to other regions.

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Gaussian process classification of superparamagnetic relaxometry data: Phantom study

Cited by 5 publications

References 17 publications

Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors

Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors

Revitalizing Postoperative Pain Management in Enhanced Recovery After Surgery via Inter-departmental Collaboration Toward Precision Medicine: A Narrative Review

Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

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