scholarly journals Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1631
Author(s):  
Il Bin Kim ◽  
Seon-Cheol Park
Keyword(s):  
Machine Learning ◽  
Personalized Medicine ◽  
Imaging Techniques ◽  
Language Game ◽  
Symptom Clusters ◽  
Machine Learning Algorithms ◽  
Depressive Syndrome ◽  
Research Network ◽  
Imaging Data ◽  
Clinical Cohort

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

scholarly journals Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

2020 ◽  
Vol 196 (10) ◽  
pp. 856-867 ◽  
Author(s):  
Martin Kocher ◽  
Maximilian I. Ruge ◽  
Norbert Galldiks ◽  
Philipp Lohmann
Keyword(s):  
Machine Learning ◽  
Brain Tumors ◽  
Brain Metastases ◽  
Imaging Techniques ◽  
Machine Learning Algorithms ◽  
Radiotherapy Planning ◽  
Who Grade ◽  
Large Variability ◽  
Malignant Brain Tumors ◽  
Integrated Diagnosis

Abstract Background Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. Methods This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. Results Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. Conclusion Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

scholarly journals The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms

Cosmetics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 67
Author(s):  
Perry Xiao ◽  
Xu Zhang ◽  
Wei Pan ◽  
Xiang Ou ◽  
Christos Bontozoglou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Imaging Techniques ◽  
Learning Algorithms ◽  
Research Work ◽  
Software Tool ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Skin Imaging ◽  
User Friendly

We present our latest research work on the development of a skin image analysis tool by using machine-learning algorithms. Skin imaging is very import in skin research. Over the years, we have used and developed different types of skin imaging techniques. As the number of skin images and the type of skin images increase, there is a need of a dedicated skin image analysis tool. In this paper, we report the development of such software tool by using the latest MATLAB App Designer. It is simple, user friendly and yet powerful. We intend to make it available on GitHub, so that others can benefit from the software. This is an ongoing project; we are reporting here what we have achieved so far, and more functions will be added to the software in the future.

scholarly journals Alzheimer’s Disease Prediction Using Long Short-Term Memory with Early-Phase 18F-Florbetaben Imaging Data

2021 ◽  
Author(s):  
Hyeon Kang ◽  
Kyung Won Park ◽  
Do-Young Kang
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Dual Phase ◽  
Imaging Data ◽  
Short Term ◽  
Term Memory ◽  
Imaging Test ◽  
Long Short Term Memory

Abstract Single amyloid-beta (Aβ) imaging test is not enough to rise to the challenge of making AD diagnosis because of Aβ-negative AD or positive cognitively normal (CN). We aimed to distinguish AD from CN with dual-phase 18F-Florbetaben (FBB) via machine learning algorithms and evaluate the AD positivity scores compared to delay-phase FBB (dFBB) which is currently adopted for AD diagnosis.A total of 264 patients (74 CN and 190 AD), who underwent FBB imaging test and neuropsychological tests were retrospectively analyzed. We compared three kinds of machine learning-based models and evaluated their performance with 4-fold cross validation.AD positivity scores estimated from dual-phase FBB showed better accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) for AD detection (ACC: 84.091 %, AUROC: 0.900) than those from dFBB imaging (ACC: 81.364 %, AUROC: 0.890). The association between predicted AD positivity and the AD occurrence were compared, the use of dual-phase FBB was highest (OR: 56.333), followed by dFBB (OR: 35.182).These results show that the combined model which interpret dual-phase FBB with long short-term memory can be used to provide a more accurate AD positivity score, which shows a closer association with AD, than the prediction with only single-phase FBB.

scholarly journals Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

2021 ◽  
Vol 15 ◽  
Author(s):  
Alexandre Routier ◽  
Ninon Burgos ◽  
Mauricio Díaz ◽  
Michael Bacci ◽  
Simona Bottani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Scalar Fields ◽  
Machine Learning Algorithms ◽  
Software Platform ◽  
Imaging Data ◽  
Clinical Neuroscience ◽  
Neuroimaging Data ◽  
Share Data

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

scholarly journals Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 958
Author(s):  
Alex Novaes Santana ◽  
Charles Novaes de Santana ◽  
Pedro Montoya
Keyword(s):  
Machine Learning ◽  
Chronic Pain ◽  
Complex Disease ◽  
Imaging Techniques ◽  
Machine Learning Algorithms ◽  
Self Report ◽  
Machine Learning Techniques ◽  
Receiver Operating Curve ◽  
Complex Data ◽  
Chronic Pain Conditions

In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer’s disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions.

scholarly journals Use of Artificial Intelligence/Machine Learning in Cancer Research During the COVID-19 Pandemic

2020 ◽  
Vol 5 (S1) ◽  
pp. 251-253
Author(s):  
Vineet Kumar Kamal ◽  
Dolly Kumari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Patients ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Imaging Data ◽  
The Past ◽  
Learning Techniques ◽  
Increased Risk ◽  
The Right

The cancer patients are more vulnerable and are at increased risk of COVID-19 and related outcomes due to their weakened immune systems, specially patients with lung cancer. Amid pandemic, the diagnosis, treatment, and care of cancer patients are very difficult and challenging due to several factors. In such situations, the latest technology in artificial intelligence (AI) or machine learning algorithms (ML) have potential to provide better diagnosis, treatments and cares of cancer patients. For example, the researches may use clinical and imaging data with machine learning techniques to make differences between coronavirus-related lung changes and those caused by immunotherapy and radiotherapy. During this pandemic, AI can be used to ensure we are getting the right patients enrolled speedily and more efficiently than the traditional, and complex ways in the past in cancer clinical trials. This is the appropriate time to go beyond the “research as usual” approach and update our research via AI and ML tools to care the cancer patients and discover new and more effective treatments.

scholarly journals A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Maria-Theodora Pandi ◽  
Maria Koromina ◽  
Iordanis Tsafaridis ◽  
Sotirios Patsilinakos ◽  
Evangelos Christoforou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Personalized Medicine ◽  
Multinomial Logistic Regression ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Routine Practice ◽  
Technological Advances ◽  
Clinical Benefits ◽  
Scoring Tool

Abstract Background The field of pharmacogenomics focuses on the way a person’s genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient’s genome, hence rendering its incorporation into clinical routine practice a realistic possibility. Methods This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation. Results All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to “Decreased function,” 41 variants as “No” function proteins, and 1 variant in “Increased function.” Conclusion Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine.

scholarly journals Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6001
Author(s):  
Zarina Rakhimberdina ◽  
Xin Liu ◽  
Tsuyoshi Murata
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Network Architecture ◽  
Data Exchange ◽  
Imaging Techniques ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Model Ensemble

With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

scholarly journals Machine learning algorithms as new screening approach for patients with endometriosis

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Sofiane Bendifallah ◽  
Anne Puchar ◽  
Stéphane Suisse ◽  
Léa Delbos ◽  
Mathieu Poilblanc ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Health Care Providers ◽  
Diagnostic Laparoscopy ◽  
Imaging Techniques ◽  
Learning Algorithms ◽  
Genomic Analysis ◽  
Machine Learning Algorithms ◽  
Care Providers ◽  
Childbearing Age

AbstractEndometriosis—a systemic and chronic condition occurring in women of childbearing age—is a highly enigmatic disease with unresolved questions. While multiple biomarkers, genomic analysis, questionnaires, and imaging techniques have been advocated as screening and triage tests for endometriosis to replace diagnostic laparoscopy, none have been implemented routinely in clinical practice. We investigated the use of machine learning algorithms (MLA) in the diagnosis and screening of endometriosis based on 16 key clinical and patient-based symptom features. The sensitivity, specificity, F1-score and AUCs of the MLA to diagnose endometriosis in the training and validation sets varied from 0.82 to 1, 0–0.8, 0–0.88, 0.5–0.89, and from 0.91 to 0.95, 0.66–0.92, 0.77–0.92, respectively. Our data suggest that MLA could be a promising screening test for general practitioners, gynecologists, and other front-line health care providers. Introducing MLA in this setting represents a paradigm change in clinical practice as it could replace diagnostic laparoscopy. Furthermore, this patient-based screening tool empowers patients with endometriosis to self-identify potential symptoms and initiate dialogue with physicians about diagnosis and treatment, and hence contribute to shared decision making.

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