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John R. Laird
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2020 – today
- 2024
- [j37]Archana Bathula, Suneet K. Gupta, Suresh Merugu, Luca Saba, Narendra N. Khanna, John R. Laird, Siva Skandha Sanagala, Rajesh Singh, Deepak Garg, Mostafa M. Fouda
, Jasjit S. Suri:
Blockchain, artificial intelligence, and healthcare: the tripod of future - a narrative review. Artif. Intell. Rev. 57(9): 238 (2024) - [j36]Mainak Biswas, Luca Saba, Mannudeep K. Kalra, Rajesh Singh, José Fernandes e Fernandes, Vijay Viswanathan, John R. Laird, Laura E. Mantella, Amer M. Johri, Mostafa M. Fouda, Jasjit S. Suri:
MultiNet 2.0: A lightweight attention-based deep learning network for stenosis measurement in carotid ultrasound scans and cardiovascular risk assessment. Comput. Medical Imaging Graph. 117: 102437 (2024) - [j35]Sushant Agarwal, Sanjay Saxena, Alessandro Carriero, Gian Luca Chabert, Gobinath Ravindran, Sudip Paul, John R. Laird, Deepak Garg, Mostafa Fatemi, Lopamudra Mohanty, Arun Kumar Dubey
COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography. Frontiers Artif. Intell. 7 (2024) - [j34]Ashish Kumar, Vivekanand Aelgani, Rubeena Vohra
Artificial intelligence bias in medical system designs: a systematic review. Multim. Tools Appl. 83(6): 18005-18057 (2024) - 2023
- [j33]Jasjit S. Suri
UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias. IEEE Access 11: 595-645 (2023) - 2022
- [j32]Jasjit S. Suri, Mrinalini Bhagawati
Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review. Comput. Biol. Medicine 142: 105204 (2022) - [j31]Mohit Agarwal
Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0. Comput. Biol. Medicine 146: 105571 (2022) - [j30]Amer M. Johri, Krishna V. Singh
Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization. Comput. Biol. Medicine 150: 106018 (2022) - 2021
- [j29]Luca Saba, Mohit Agarwal
Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs. Int. J. Comput. Assist. Radiol. Surg. 16(3): 423-434 (2021) - [j28]Mainak Biswas, Luca Saba, Tomaz Omerzu, Amer M. Johri, Narendra N. Khanna
A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework. J. Digit. Imaging 34(3): 581-604 (2021) - [j27]Mohit Agarwal
Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application. Medical Biol. Eng. Comput. 59(3): 511-533 (2021) - [j26]Luca Saba
A Multicenter Study on Carotid Ultrasound Plaque Tissue Characterization and Classification Using Six Deep Artificial Intelligence Models: A Stroke Application. IEEE Trans. Instrum. Meas. 70: 1-12 (2021) - 2020
- [j25]Mainak Biswas, Luca Saba, Shubhro Chakrabartty, Narender N. Khanna
Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment. Comput. Biol. Medicine 123: 103847 (2020) - [j24]Jasjit S. Suri, Anudeep Puvvula
COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review. Comput. Biol. Medicine 124: 103960 (2020) - [j23]Siva Skandha Sanagala
3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0. Comput. Biol. Medicine 125: 103958 (2020) - [j22]Ankush D. Jamthikar, Deep Gupta
Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound. Comput. Biol. Medicine 126: 104043 (2020)
2010 – 2019
- 2019
- [j21]Narendra N. Khanna
Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study. Comput. Biol. Medicine 105: 125-143 (2019) - [j20]Elisa Cuadrado Godia
Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach. Comput. Biol. Medicine 108: 182-195 (2019) - [j19]Mainak Biswas
Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk. Medical Biol. Eng. Comput. 57(2): 543-564 (2019) - [j18]Narendra N. Khanna
Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1.0. Medical Biol. Eng. Comput. 57(7): 1553-1566 (2019) - 2018
- [j17]Mainak Biswas
Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort. Comput. Biol. Medicine 98: 100-117 (2018) - [j16]Elisa Cuadrado Godia
Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort. Comput. Biol. Medicine 101: 128-145 (2018) - [j15]P. Krishna Kumar
State-of-the-art review on automated lumen and adventitial border delineation and its measurements in carotid ultrasound. Comput. Methods Programs Biomed. 163: 155-168 (2018) - 2017
- [j14]Tadashi Araki, Pankaj Kumar Jain
Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm. Comput. Biol. Medicine 80: 77-96 (2017) - [j13]Sumit K. Banchhor, Narendra D. Londhe
Well-balanced system for coronary calcium detection and volume measurement in a low resolution intravascular ultrasound videos. Comput. Biol. Medicine 84: 168-181 (2017) - [j12]Sumit K. Banchhor, Narendra D. Londhe
Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm. Comput. Biol. Medicine 91: 198-212 (2017) - [j11]Luca Saba, Sumit K. Banchhor, Narendra D. Londhe
Web-based accurate measurements of carotid lumen diameter and stenosis severity: An ultrasound-based clinical tool for stroke risk assessment during multicenter clinical trials. Comput. Biol. Medicine 91: 306-317 (2017) - [j10]Nobutaka Ikeda, Nilanjan Dey
Automated segmental-IMT measurement in thin/thick plaque with bulb presence in carotid ultrasound from multiple scanners: Stroke risk assessment. Comput. Methods Programs Biomed. 141: 73-81 (2017) - [j9]Luca Saba, Pankaj Kumar Jain
Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm. J. Medical Syst. 41(6): 98:1-98:31 (2017) - [j8]P. Krishna Kumar
Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach. Medical Biol. Eng. Comput. 55(8): 1415-1434 (2017) - 2016
- [j7]Luca Saba, Sumit K. Banchhor, Harman S. Suri, Narendra D. Londhe
Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial. Comput. Biol. Medicine 75: 217-234 (2016) - [j6]Tadashi Araki, Nobutaka Ikeda, Devarshi Shukla
A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens. Comput. Methods Programs Biomed. 124: 161-179 (2016) - [j5]Tadashi Araki, Nobutaka Ikeda, Devarshi Shukla
PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology. Comput. Methods Programs Biomed. 128: 137-158 (2016) - [j4]Sumit K. Banchhor, Tadashi Araki, Narendra D. Londhe
Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach. Comput. Methods Programs Biomed. 134: 237-258 (2016) - [j3]Tadashi Araki, Sumit K. Banchhor, Narendra D. Londhe
Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos. J. Medical Syst. 40(3): 51:1-51:20 (2016) - [j2]Tadashi Araki, P. Krishna Kumar
Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches. J. Medical Syst. 40(7): 182:1-182:19 (2016) - 2015
- [j1]Tadashi Araki, Nobutaka Ikeda, Nilanjan Dey
A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound. Comput. Methods Programs Biomed. 118(2): 158-172 (2015)
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