Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm

Shrivastava, Vimal K.; Londhe, Narendra D.; Sonawane, Rajendra S.; Suri, Jasjit S.

doi:10.1016/j.eswa.2015.03.014

Cited by 82 publications

(45 citation statements)

References 25 publications

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“…Machine-learning approaches have been applied to other types of -omics data, to study or classify psoriasis: random forests have been used to predict psoriasis from transcriptome data 37 and electronic records 38 ; support vector machines have been used to predict psoriasis from dermoscopy images 39 . Here, we applied machine-learning toward the production of a metric for predicting the risk of psoriasis subtypes among psoriasis patients, using purely genetic data.…”

Section: Discussionmentioning

confidence: 99%

Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients

et al. 2018

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Psoriatic arthritis (PsA) is a complex chronic musculoskeletal condition that occurs in ~30% of psoriasis patients. Currently, no systematic strategy is available that utilizes the differences in genetic architecture between PsA and cutaneous-only psoriasis (PsC) to assess PsA risk before symptoms appear. Here, we introduce a computational pipeline for predicting PsA among psoriasis patients using data from six cohorts with >7000 genotyped PsA and PsC patients. We identify 9 new loci for psoriasis or its subtypes and achieve 0.82 area under the receiver operator curve in distinguishing PsA vs. PsC when using 200 genetic markers. Among the top 5% of our PsA prediction we achieve >90% precision with 100% specificity and 16% recall for predicting PsA among psoriatic patients, using conditional inference forest or shrinkage discriminant analysis. Combining statistical and machine-learning techniques, we show that the underlying genetic differences between psoriasis subtypes can be used for individualized subtype risk assessment.

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Section: Discussionmentioning

confidence: 99%

Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients

et al. 2018

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“…the abovementioned challenges to provide reliability and robustness. The spirit of this study was motivated by the work of Suri et al, who applied machine intelligence in different fields of medicine including gynecology, urology, dermatology, neurology [20][21][22][23], and recently in endocrinology [24].…”

Section: Introductionmentioning

confidence: 99%

Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort

Biswas

Kuppili

Araki

et al. 2018

Computers in Biology and Medicine

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“…The ability of CADx to perform almost nearly real-time classification allows it to be utilized clinically as a diagnostic decision tool for the physician [69,70]. The integral part of a CADx system is the machine learning model.…”

Section: Machine Learning For Stroke Risk Stratificationmentioning

confidence: 99%

A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework

Sharma

Gupta

Kumar

et al. 2015

Curr Atheroscler Rep

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Cardiovascular diseases (including stroke and heart attack) are identified as the leading cause of death in today's world. However, very little is understood about the arterial mechanics of plaque buildup, arterial fibrous cap rupture, and the role of abnormalities of the vasa vasorum. Recently, ultrasonic echogenicity characteristics and morphological characterization of carotid plaque types have been shown to have clinical utility in classification of stroke risks. Furthermore, this characterization supports aggressive and intensive medical therapy as well as procedures, including endarterectomy and stenting. This is the first state-of-the-art review to provide a comprehensive understanding of the field of ultrasonic vascular morphology tissue characterization. This paper presents fundamental and advanced ultrasonic tissue characterization and feature extraction methods for analyzing plaque. Additionally, the paper shows how the risk stratification is achieved using machine learning paradigms. More advanced methods need to be developed which can segment the carotid artery walls into multiple regions such as the bulb region and areas both proximal and distal to the bulb. Furthermore, multimodality imaging is needed for validation of such advanced methods for stroke and cardiovascular risk stratification.

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Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm

Cited by 82 publications

References 25 publications

Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients

Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients

Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort

A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework

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