1,011 Hits in 5.8 sec

Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs [article]

Sara Ranjbar, Kyle W. Singleton, Lee Curtin, Susan Christine Massey, Andrea Hawkins-Daarud, Pamela R. Jackson, Kristin R. Swanson
2019 arXiv   pre-print
Fluid intelligence (Gf) has been defined as the ability to reason and solve previously unseen problems.  ...  We trained sex-specific convolutional neural net (CNN) and random forest models to predict Gf.  ...  In the second, we trained random forest regression models using the volumetric features from image-atlas alignment along with the age and sex of subjects.  ... 
arXiv:1908.02333v1 fatcat:p7sdkjmicbhbpjzcsfpapcl3fu

Beyond brain age: Empirically-derived proxy measures of mental health [article]

Kamalaker Dadi, Gael Varoquaux, Josselin Houenou, Danilo Bzdok, Bertrand Thirion, Denis A Engemann
2020 bioRxiv   pre-print
Similar to how brain age captures biological aging, intelligence and neuroticism may provide empirical proxies for mental health.  ...  Constructs such as intelligence or neuroticism are typically assessed by specialized workforce through tailored questionnaires and tests.  ...  To approximate age, fluid intelligence and neuroticism, we applied random-forest regression on sociodemographic data and brain images as inputs.  ... 
doi:10.1101/2020.08.25.266536 fatcat:7zeoia5225bf7ota4x775uwlcm

Advanced Methods for Connectome-Based Predictive Modeling of Human Intelligence: A Novel Approach Based on Individual Differences in Cortical Topography [article]

Evan D. Anderson, Ramsey Wilcox, Anuj Nayak, Christopher Zwilling, Pablo Robles-Granda, Been Kim, Lav R. Varshney, Aron K. Barbey
2022 arXiv   pre-print
Individual differences in human intelligence can be modeled and predicted from in vivo neurobiological connectivity.  ...  These modifications produce a novel predictive modeling framework that leverages individual differences in cortical tractography to generate accurate regression predictions of intelligence scores.  ...  bagged random forests [14] .  ... 
arXiv:2203.00707v2 fatcat:qbkdio4oojcldalr3xu6l5qwna

Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging

Jae-Young Kim, Dongwook Kim, Kug Jin Jeon, Hwiyoung Kim, Jong-Ki Huh
2021 Scientific Reports  
The MLP and random forest were also superior to previously reported results using MRI (AUC 0.808) and MRI-based nomogram (AUC 0.889).  ...  Data containing those features were applied to build and validate prediction models using random forest and multilayer perceptron (MLP) techniques, the latter using the Keras framework, a recent deep learning  ...  The data derived from these features were used to build a prediction model. Disc shapes. "Biconcave" refers to the normal disc structure and position.  ... 
doi:10.1038/s41598-021-86115-3 pmid:33758266 fatcat:f5wqh3gf3rh7plbph2myfdwt4a

Predicting Short-term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI

Santiago Cepeda, Angel Pérez-Nuñez, Sergio García-García, Daniel García-Pérez, Ignacio Arrese, Luis Jiménez-Roldán, Manuel García-Galindo, Pedro González, María Velasco-Casares, Tomas Zamora, Rosario Sarabia
2021 Cancers  
Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study.  ...  Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology.  ...  We also highlight that the results of our predictive models have been achieved using only structural MRI [14] .  ... 
doi:10.3390/cancers13205047 pmid:34680199 pmcid:PMC8533879 fatcat:cujv2vq33nhgpa5vrmuo3wevuu

Population modeling with machine learning can enhance measures of mental health - Open-Data Replication [article]

Ty O Easley, Ruiqi Chen, Kayla Hannon, Rosie K Dutt, Janine D Bijsterbosch
2022 bioRxiv   pre-print
Efforts to predict trait phenotypes based on functional MRI data from large cohorts have been hampered by low prediction accuracy and/or small effect sizes.  ...  ., extended fMRI features, averaged target phenotype, balanced target phenotype distribution) led to a three-fold increase in prediction accuracy for fluid intelligence compared to prior work.  ...  For example, Dadi et al used Random Forest Regression in N=10,000 participants from the UK Biobank (UKB) data to predict trait-level phenotypes of neuroticism and fluid intelligence, and reported a maximum  ... 
doi:10.1101/2022.04.04.487069 fatcat:s6x2ygiqtnf73imb3btohwjuvq

Exploring the methods on early detection of Alzheimer's disease

2020 International journal of recent technology and engineering  
Machine learning is a part of artificial intelligence involving probabilistic and optimization techniques such as neural networks that prepares pc's to gain a model from complex datasets.  ...  This paper also throws light on the datasets being used and how these preprocessing steps and different classifiers attribute to increase of accuracy in prediction of AD.  ...  LB1 -1627 LB2 -110 Structural MRI Random forest 73% 0.82 Moscoso,, 2019 [22] ADNI 1 NC -124 AD -124 sMCI -134 pMCI -89 MRI Logistic regression - 73% at 2 year 84% at 5 year  ... 
doi:10.35940/ijrte.a2391.059120 fatcat:p4o6me5fafb6hdzhxybohljmgu

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease

Chun-Hung Chang, Chieh-Hsin Lin, Hsien-Yuan Lane
2021 International Journal of Molecular Sciences  
Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy  ...  Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis.  ...  The researchers employed four machine learning algorithms (SVM, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model for distinguishing patients with MCI or AD from  ... 
doi:10.3390/ijms22052761 pmid:33803217 fatcat:33ixois7qjgjtmdwik3vla5xke

Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI

Tak Sung Heo, Yu Seop Kim, Jeong Myeong Choi, Yeong Seok Jeong, Soo Young Seo, Jun Ho Lee, Jin Pyeong Jeon, Chulho Kim
2020 Journal of Personalized Medicine  
Random forest was the best classifier (0.782 of AUROC) using a word-level approach. Overall, the document-level approach exhibited better performance than did the word- or sentence-level approaches.  ...  Brain magnetic resonance imaging (MRI) is useful for predicting the outcome of patients with acute ischemic stroke (AIS).  ...  In the word-level approach, we used the least absolute shrinkage and selection operator (LASSO) regression, single decision tree, random forest (RF), and support vector machine (SVM) techniques for the  ... 
doi:10.3390/jpm10040286 pmid:33339385 fatcat:l3v7vzh2urfa5agso6ct73nzzu

Metrics of brain network architecture capture the impact of disease in children with epilepsy

Michael J. Paldino, Wei Zhang, Zili D. Chu, Farahnaz Golriz
2017 NeuroImage: Clinical  
Conclusions: We observed that a machine learning algorithm accurately predicted epilepsy duration based on global metrics of network architecture derived from resting state fMRI.  ...  The aim of this study was to measure the potential of global metrics of network architecture derived from resting state functional MRI to capture the impact of epilepsy on the developing brain.  ...  The control model was then used to predict epilepsy duration using an otherwise identical Random Forest algorithm. The relationship to true epilepsy duration was assessed using linear regression.  ... 
doi:10.1016/j.nicl.2016.12.005 pmid:28003958 pmcid:PMC5157798 fatcat:7b3ogiw3r5gr5piwzlo62hr6gq

Combining electrophysiology with MRI enhances learning of surrogate-biomarkers

Denis Alexander Engemann, Oleh Kozynets, David Sabbagh, Guillaume Lemaitre, Gaël Varoquaux, Franziskus Liem, Alexandre Gramfort
2019 biorxiv/medrxiv  
Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as surrogate biomarker in 674 subjects from the Cam-CAN.  ...  Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods.  ...  To correct for the necessarily biased linear model, we then used non-linear random forest regressor with age predictions from the linear model as lower-dimensional input features.  ... 
doi:10.1101/856336 fatcat:vjisc5h2yrbrjprqfgf23jwjgy

Radiological images and machine learning: Trends, perspectives, and prospects

Zhenwei Zhang, Ervin Sejdić
2019 Computers in Biology and Medicine  
Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance  ...  Huynh predicted CT images from MRI data using a structured random forest instead of a classical random forest [70] .  ...  A structured random forest is an extension of a random forest, which predicts structured outputs instead of scalar outputs [216, 217] .  ... 
doi:10.1016/j.compbiomed.2019.02.017 pmid:31054502 pmcid:PMC6531364 fatcat:tcyorm6g3ff6dg7ty2ubtqorjq

Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms

Zenghua Ren, Yudan Hu, Ling Xu
2019 Respiratory Research  
machine (SVM) and random forest (RF) were established and their respective diagnostic performances were calculated.  ...  In recent years, artificial intelligence (AI) machine learning algorithms have started being used to an increasing extent in disease diagnosis due to the high level of efficiency, objectivity, and accuracy  ...  Availability of data and materials The datasets used or analyzed during the current study are available per reasonable request from the corresponding author.  ... 
doi:10.1186/s12931-019-1197-5 pmid:31619240 pmcid:PMC6796452 fatcat:2aqxu6fgxnelbcxdonldte6lz4

Distinguishing Adolescents With Conduct Disorder From Typically Developing Youngsters Based on Pattern Classification of Brain Structural MRI

Jianing Zhang, Weixiang Liu, Jing Zhang, Qiong Wu, Yidian Gao, Yali Jiang, Junling Gao, Shuqiao Yao, Bingsheng Huang
2018 Frontiers in Human Neuroscience  
The significantly different regional GM volumes were then extracted as features, and input into three ML classifiers: logistic regression, random forest and support vector machine (SVM).  ...  We trained and tested these ML models for classifying CD from HCs by using fivefold cross-validation (CV).  ...  ACKNOWLEDGMENTS The study was funded by the National Natural Science Foundation of China (No. 81471384) and the Seed Funding from Scientific and Technical Innovation Council of Shenzhen Government (No.  ... 
doi:10.3389/fnhum.2018.00152 pmid:29740296 pmcid:PMC5925967 fatcat:rfl2qv7rv5aklogcahhwionudq

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives

Octavian Sabin Tătaru, Mihai Dorin Vartolomei, Jens J. Rassweiler, Oșan Virgil, Giuseppe Lucarelli, Francesco Porpiglia, Daniele Amparore, Matteo Manfredi, Giuseppe Carrieri, Ugo Falagario, Daniela Terracciano, Ottavio de Cobelli (+3 others)
2021 Diagnostics  
Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects.  ...  The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the  ...  [86] used a CNN algorithm, random forest, from synthetic CT images generated from MRI images.  ... 
doi:10.3390/diagnostics11020354 pmid:33672608 pmcid:PMC7924061 fatcat:jqktyzjrhjh2jaxvlpk3pomube
« Previous Showing results 1 — 15 out of 1,011 results