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

Agarwal, Mohit; Saba, Luca; Gupta, Suneet Kr.; Johri, Amer M.; Khanna, Narendra N.; Mavrogeni, Sophie; Laird, John R.; Pareek, Gyan; Miner, Martin; Sfikakis, Petros P.; Protogerou, Athanasios; Sharma, Aditya; Viswanathan, Vijay; Kitas, George D.; Nicolaides, Andrew; Suri, Jasjit S.

doi:10.1007/s11517-021-02322-0

Cited by 46 publications

(30 citation statements)

References 80 publications

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“…These models have considered tissue characterization approaches since they analyze the tissue data for better feature extraction to evaluate for ground vs. background, thus are more akin to a tissue characterization in classification framework [30,37]. Our group has strong experience in tissue characterization approaches with different AI models and applications for classification using ML frameworks such as plaque, liver, thyroid, breast [21,28,30,[63][64][65][66][67][68], and DL framework [1,36,69,70]. These three AI models were trained using the GT annotated data from the two observers.…”

Section: Discussionmentioning

confidence: 99%

“…The WHO's International Health Regulations and Emergency Committee (IHREC) proclaimed COVID-19 a "public health emergency of international significance" or "pandemic" on 30 January 2020. More than 231 million people have been infected worldwide, and nearly 4.7 million people have died due to COVID-19 [1]. Although this "severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2) virus specifically targets the pulmonary and vascular system, it has the potential to travel through the body and lead to complications such as pulmonary embolism [2], myocardial infarction, stroke, or mesenteric ischemia [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Suri¹,

Agarwal²,

Elavarthi³

et al. 2021

Diagnostics

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Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Suri¹,

Agarwal²,

Elavarthi³

et al. 2021

Diagnostics

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“…Some of our findings may not be specific of WD but could be associated with liver fibrosis and portal hypertension in general. However, the proposed algorithm could have incremental value if added to existing diagnostic parameters of altered copper metabolism (except ceruloplasmin) or findings from liver or brain imaging [58], and it could streamline the diagnostic process. It could be argued that adding histological reports may improve the accuracy of the ANN-based algorithm.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence

et al. 2021

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WD is caused by ATP7B variants disrupting copper efflux resulting in excessive copper accumulation mainly in liver and brain. The diagnosis of WD is challenged by its variable clinical course, onset, morbidity, and ATP7B variant type. Currently it is diagnosed by a combination of clinical symptoms/signs, aberrant copper metabolism parameters (e.g., low ceruloplasmin serum levels and high urinary and hepatic copper concentrations), and genetic evidence of ATP7B mutations when available. As early diagnosis and treatment are key to favorable outcomes, it is critical to identify subjects before the onset of overtly detrimental clinical manifestations. To this end, we sought to improve WD diagnosis using artificial neural network algorithms (part of artificial intelligence) by integrating available clinical and molecular parameters. Surprisingly, WD diagnosis was based on plasma levels of glutamate, asparagine, taurine, and Fischer’s ratio. As these amino acids are linked to the urea–Krebs’ cycles, our study not only underscores the central role of hepatic mitochondria in WD pathology but also that most WD patients have underlying hepatic dysfunction. Our study provides novel evidence that artificial intelligence utilized for integrated analysis for WD may result in earlier diagnosis and mechanistically relevant treatments for patients with WD.

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“…In future, there could be a need to design an automated ICA segmentation system [85]. Another possibility would be to improve the CNN by an improved DCNN model, where the rectified linear unit (ReLU) activation function was modified, ensuring "differentiable at zero" [38]. There are dense networks such as DenseNet121, DenseNet169, and DenseNet201 that could be tried and compared [39].…”

Section: Strengths/weakness/extensionsmentioning

confidence: 99%

“…Deep learning (DL) is a subset of AI that has revolutionized image classification methods [34][35][36]. Among all the different DL techniques available, transfer learning (TL) solves the high-performance computational challenges required for images rich with data [37][38][39]. In addition to the computational problem, TL reduces the time taken for training the model compared with DL [40].…”

Section: Introductionmentioning

confidence: 99%

Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification

et al. 2021

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Background and Purpose: Only 1–2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches—a class of Atheromatic™ 2.0 TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i–ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv–v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.

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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

Cited by 46 publications

References 80 publications

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence

Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification

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