scholarly journals Digital imaging biomarkers feed machine learning for melanoma screening

2016 ◽  
Vol 26 (7) ◽  
pp. 615-618 ◽  
Author(s):  
Daniel S. Gareau ◽  
Joel Correa da Rosa ◽  
Sarah Yagerman ◽  
John A. Carucci ◽  
Nicholas Gulati ◽  
...  
Keyword(s):  
Machine Learning ◽  
Digital Imaging ◽  
Imaging Biomarkers ◽  
Melanoma Screening

scholarly journals LONG-TERM PREDICTION OF CARDIAC EVENTS IN ASYMPTOMATIC SUBJECTS BY INTEGRATING NOVEL CIRCULATING AND IMAGING BIOMARKERS USING MACHINE LEARNING

2020 ◽  
Vol 75 (11) ◽  
pp. 1580
Author(s):  
Andrew Lin ◽  
Balaji Tamarappoo ◽  
Frederic Commandeur ◽  
Priscilla McElhinney ◽  
Sebastien Cadet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imaging Biomarkers ◽  
Cardiac Events ◽  
Term Prediction ◽  
Asymptomatic Subjects ◽  
Long Term Prediction

scholarly journals Chlorophyll estimation in soybean leaves infield with smartphone digital imaging and machine learning

2020 ◽  
Vol 174 ◽  
pp. 105433
Author(s):  
Oveis Hassanijalilian ◽  
C. Igathinathane ◽  
Curt Doetkott ◽  
Sreekala Bajwa ◽  
John Nowatzki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Digital Imaging ◽  
Soybean Leaves

scholarly journals Deep Neural Survival Networks for Cardiovascular Risk Prediction: The Multi-Ethnic Study of Atherosclerosis (MESA)

2021 ◽  
Author(s):  
Quincy A Hathaway ◽  
Naveena Yanamala ◽  
Matthew J Budoff ◽  
Partho P Sengupta ◽  
Irfan Zeb
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Deep Learning ◽  
Risk Prediction ◽  
External Validation ◽  
Imaging Biomarkers ◽  
Atherosclerotic Cardiovascular Disease ◽  
Ethnic Study ◽  
Ascvd Risk ◽  
Traditional Risk Factors

Background: There is growing interest in utilizing machine learning techniques for routine atherosclerotic cardiovascular disease (ASCVD) risk prediction. We investigated whether novel deep learning survival models can augment ASCVD risk prediction over existing statistical and machine learning approaches. Methods: 6,814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) were followed over 16 years to assess incidence of all-cause mortality (mortality) or a composite of major adverse events (MAE). Features were evaluated within the categories of traditional risk factors, inflammatory biomarkers, and imaging markers. Data was split into an internal training/testing (four centers) and external validation (two centers). Both machine learning (COXPH, RSF, and lSVM) and deep learning (nMTLR and DeepSurv) models were evaluated. Results: In comparison to the COXPH model, DeepSurv significantly improved ASCVD risk prediction for MAE (AUC: 0.82 vs. 0.79, P≤0.001) and mortality (AUC: 0.86 vs. 0.80, P≤0.001) with traditional risk factors alone. Implementing non-categorical NRI, we noted a 65% increase in correct reclassification compared to the COXPH model for both MAE and mortality (P≤0.05). Assessing the relative risk of participants, DeepSurv was the only learning algorithm to develop a significantly improved risk score criteria, which outcompeted COXPH for both MAE (4.07 vs. 2.66, P≤0.001) and mortality (6.28 vs. 4.67, P=0.014). The addition of inflammatory or imaging biomarkers to traditional risk factors showed minimal/no significant improvement in model prediction. Conclusion: DeepSurv can leverage simple office-based clinical features alone to accurately predict ASCVD risk and cardiovascular outcomes, without the need for additional features, such as inflammatory and imaging biomarkers.

scholarly journals The role of diffusion tensor imaging metrics in machine learning-based characterisation of paediatric brain tumors and their practicality for multicentre clinical assessment

2021 ◽  
Vol 23 (Supplement_4) ◽  
pp. iv1-iv2
Author(s):  
Heather Rose ◽  
Huijun Li ◽  
Christopher D Bennett ◽  
Jan Novak ◽  
Yu Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumours ◽  
Anisotropic Diffusion ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Imaging Biomarkers ◽  
Significant Difference ◽  
Mri Scans ◽  
Leave One Out ◽  
Paediatric Brain Tumours

Abstract Aims Magnetic resonance imaging (MRI) is a valuable tool for non-invasive diagnosis of paediatric brain tumours. The rarity of the disease dictates multi-centre studies and imaging biomarkers that are robust to protocol variability. We investigated diffusion tensor MRI (DT-MRI), combined with machine learning, as an aid to diagnosis and evaluated the robustness of the imaging metrics. Method A multi-centre cohort of 52 clinical DT-MRI scans (20 medulloblastomas (MB), 21 pilocytic astrocytomas (PA), 11 ependymomas (EP)) were analysed retrospectively. Histograms for regions of solid tumour for fractional anisotropy (FA), mean diffusivity (MD), pure anisotropic diffusion (q) and pure isotropic diffusion (p) were compared to assess diagnostic capability. Linear discriminate analysis (LDA) was used for classification and validated using leave-one-out-cross-validation (LOOCV). Results Histogram medians for FA, MD, q and p were all different between tumor groups (P<.0001, Kruskal Wallis test). Median MD, p and q values were highest in PA, then EP and lowest in MB (P<.0001, Pairwise Wilcox test). FA median was higher for EP than PA (P=.004) with no significant difference between EP and MB (P=.591). ROC analysis showed that median MD, q and p perform best as a diagnostic marker (AUC= 0.92 to 0.99). LOOCV showed an overall accuracy of the LDA classification, ranging between 67% - 87%. FA values were highly dependent on protocol parameters, whereas pure anisotropic diffusion, q, was not. Conclusion DT-MRI metrics from multi-centre acquisitions can classify paediatric brain tumours. FA is the least robust metric to protocol variability and q provides the most robust quantification of anisotropic behaviour.

scholarly journals Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics

2021 ◽  
pp. 1-44
Author(s):  
Andrew Cwiek ◽  
Sarah M. Rajtmajer ◽  
Bradley Wyble ◽  
Vasant Honavar ◽  
Emily Grossner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Patient Outcomes ◽  
Predictive Models ◽  
Resting State ◽  
Cross Validation ◽  
Functional Neuroimaging ◽  
Imaging Biomarkers ◽  
Brain Disorders ◽  
Neuroimaging Data ◽  
Key Steps

Abstract In this critical review, we examine the application of predictive models, e.g. classifiers, trained using Machine Learning (ML) to assist in interpretation of functional neuroimaging data. Our primary goal is to summarize how ML is being applied and critically assess common practices. Our review covers 250 studies published using ML and resting-state functional MRI (fMRI) to infer various dimensions of the human functional connectome. Results for hold-out (“lockbox”) performance was, on average, ~13% less accurate than performance measured through cross-validation alone, highlighting the importance of lockbox data which was included in only 16% of the studies. There was also a concerning lack of transparency across the key steps in training and evaluating predictive models. The summary of this literature underscores the importance of the use of a lockbox and highlights several methodological pitfalls that can be addressed by the imaging community. We argue that, ideally, studies are motivated both by the reproducibility and generalizability of findings as well as the potential clinical significance of the insights. We offer recommendations for principled integration of machine learning into the clinical neurosciences with the goal of advancing imaging biomarkers of brain disorders, understanding causative determinants for health risks, and parsing heterogeneous patient outcomes.

AI-driven imaging biomarkers for sensory cue integration during melanoma screening (Conference Presentation)

2020 ◽  
Author(s):  
Daniel S. Gareau ◽  
Charles Vrattos ◽  
James Browning ◽  
Samantha R. Lish ◽  
Benjamin Firester ◽  
...  
Keyword(s):  
Imaging Biomarkers ◽  
Cue Integration ◽  
Sensory Cue ◽  
Melanoma Screening

scholarly journals Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration

2018 ◽  
Vol 2 (1) ◽  
pp. 24-30 ◽  
Author(s):  
Ursula Schmidt-Erfurth ◽  
Hrvoje Bogunovic ◽  
Amir Sadeghipour ◽  
Thomas Schlegl ◽  
Georg Langs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Macular Degeneration ◽  
Prognostic Value ◽  
Age Related Macular Degeneration ◽  
Imaging Biomarkers ◽  
Age Related
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