Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation

Jain, Pankaj K.; Agarwal, Sushant; Saba, Luca; Chabert, Gian Luca; Carriero, Alessandro; Paschè, Alessio; Danna, Pietro; Mehmedović, Armin; Faa, Gavino; Jujaray, Tanay; Singh, Inder; Khanna, Narendra N.; Laird, John R.; Sfikakis, Petros P.; Agarwal, Vikas; Teji, Jagjit S.; Yadav, Rajanikant R; Nagy, Ferenc; Kincses, Zsigmond Tamás; Ruzsa, Zoltán; Višković, Klaudija; Kalra, Mannudeep K.

doi:10.1007/s10916-022-01850-y

Cited by 13 publications

(9 citation statements)

References 99 publications

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“…Similarly, the mean difference between the unseen and seen AUC for the EDL models was ~2.4% . Note that the criterion for a robust design, leading to superior generalizability, was that the difference between seen and unseen analysis be less than 3% to 5% [ 54 , 55 , 56 ]; our system design demonstrates results less than 3% , which qualifies it as a robust, generalizable, and stable design, which is also part of our running hypothesis.…”

Section: Resultsmentioning

confidence: 88%

“…While individual deep learning models have shown limited success in detecting depression, combining them into hybrid deep learning models has been shown to improve performance and overcome data scarcity [ 50 ]. By leveraging multiple architectures, Hybrid models can address domain-specific challenges and improve accuracy in tasks such as detecting depression [ 51 , 52 , 53 , 54 ]. Using these spirits of HDL, we finally constructed three hybrid deep learning (HDL) models: a CNN-LSTM, a CNN-BiLSTM, and a BERT-BiLSTM.…”

Section: Methodsmentioning

confidence: 99%

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Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm

Singh

Fouda

et al. 2023

Diagnostics

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Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently, attention mechanisms have been introduced in SDL. We hypothesize that attention-enabled EDL (aeEDL) architectures are superior compared to attention-not-enabled SDL (aneSDL) or aeSDL models. We designed EDL-based architectures with attention blocks to build eleven kinds of SDL model and five kinds of EDL model on four domain-specific datasets. We scientifically validated our models by comparing “seen” and “unseen” paradigms (SUP). We benchmarked our results against the SemEval (2016) sentimental dataset and established reliability tests. The mean increase in accuracy for EDL over their corresponding SDL components was 4.49%. Regarding the effect of attention block, the increase in the mean accuracy (AUC) of aeSDL over aneSDL was 2.58% (1.73%), and the increase in the mean accuracy (AUC) of aeEDL over aneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the mean aneEDL was greater than aneSDL by 4.82% (3.71%), and the mean aeEDL was greater than aeSDL by 5.06% (4.81%). For the benchmarking dataset (SemEval), the best-performing aeEDL model (ALBERT+BERT-BiLSTM) was superior to the best aeSDL (BERT-BiLSTM) model by 3.86%. Our scientific validation and robust design showed a difference of only 2.7% in SUP, thereby meeting the regulatory constraints. We validated all our hypotheses and further demonstrated that aeEDL is a very effective and generalized method for detecting symptoms of depression in cross-domain settings.

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Section: Resultsmentioning

confidence: 88%

Section: Methodsmentioning

confidence: 99%

Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm

Singh

Fouda

et al. 2023

Diagnostics

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“…GenAI aids in diagnostic accuracy, although its focus on higher value creation in health care is limited. The articles in this review reported that they used deep learning (34/161, 21.1%) [ 49 , 59 , 60 , 62 , 63 , 65 , 68 , 71 , 79 , 89 , 100 , 107 , 108 , 111 , 115 , 123 , 125 , 130 - 145 ], machine learning (9/161, 5.6%) [ 53 , 55 , 83 , 91 , 110 , 146 - 149 ], and image analysis approaches of GenAI during the assistance process (13/161, 8.1%) [ 68 , 88 , 104 , 110 , 111 , 114 , 116 , 119 , 133 , 135 , 138 , 150 , 151 ]. Knowledge access using GenAI has the potential to enable more options and flexibility in serving patients.…”

Section: Resultsmentioning

confidence: 99%

“…Deep learning (34/161, 21.1%) [ 49 , 59 , 60 , 62 , 63 , 65 , 68 , 71 , 79 , 89 , 100 , 107 , 108 , 111 , 115 , 123 , 125 , 130 - 145 ]…”

Section: Methodsunclassified

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review

Yim,

Khuntia,

Parameswaran

et al. 2024

JMIR Med Inform

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Background Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients’ families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. Objective This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. Methods We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. Results Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. Conclusions GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service–level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

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“…The use of DL algorithms allows the extraction of highly minute details that are not even visible to the human eye [26]. As a result, DL techniques are widely employed in medical image analysis for tasks including image registration [53], segmentation [54][55][56][57], and classification [58][59][60][61][62]. The most frequent type of brain tumor in people is glioma.…”

Section: Introductionmentioning

confidence: 99%

Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data

et al. 2023

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The biopsy is a gold standard method for tumor grading. However, due to its invasive nature, it has sometimes proved fatal for brain tumor patients. As a result, a non-invasive computer-aided diagnosis (CAD) tool is required. Recently, many magnetic resonance imaging (MRI)-based CAD tools have been proposed for brain tumor grading. The MRI has several sequences, which can express tumor structure in different ways. However, a suitable MRI sequence for brain tumor classification is not yet known. The most common brain tumor is ‘glioma’, which is the most fatal form. Therefore, in the proposed study, to maximize the classification ability between low-grade versus high-grade glioma, three datasets were designed comprising three MRI sequences: T1-Weighted (T1W), T2-weighted (T2W), and fluid-attenuated inversion recovery (FLAIR). Further, five well-established convolutional neural networks, AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 were adopted for tumor classification. An ensemble algorithm was proposed using the majority vote of above five deep learning (DL) models to produce more consistent and improved results than any individual model. Five-fold cross validation (K5-CV) protocol was adopted for training and testing. For the proposed ensembled classifier with K5-CV, the highest test accuracies of 98.88 ± 0.63%, 97.98 ± 0.86%, and 94.75 ± 0.61% were achieved for FLAIR, T2W, and T1W-MRI data, respectively. FLAIR-MRI data was found to be most significant for brain tumor classification, where it showed a 4.17% and 0.91% improvement in accuracy against the T1W-MRI and T2W-MRI sequence data, respectively. The proposed ensembled algorithm (MajVot) showed significant improvements in the average accuracy of three datasets of 3.60%, 2.84%, 1.64%, 4.27%, and 1.14%, respectively, against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50.

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Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation

Cited by 13 publications

References 99 publications

Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm

Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review

Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data

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