Comparison between Deep Learning and Conventional Machine Learning in Classifying Iliofemoral Deep Venous Thrombosis upon CT Venography

Hwang, Jung Han; Seo, Je Hyun; Kim, Jeong Ho; Park, Suyoung; Kim, Young Jae; Kim, Kwang Gi

doi:10.3390/diagnostics12020274

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…It was clear that the DL models had better performance than the radiomics-based ML model in differentiating ASPs from SPs (AUC = 0.9294 vs. AUC = 0.8853). These results were consistent with the findings for Mantle Cell Lymphoma ( 27 ) and Deep Vein Thrombosis ( 28 ) that DL models had better diagnostic performance than radiomics-based ML models. This was due to the fact that DL extracts more representative high-level abstract features from the raw data, while machine learning requires manual feature selection and design.…”

Section: Discussionsupporting

confidence: 91%

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques

Gui

Cao

Zhang

et al. 2023

Front. Cardiovasc. Med.

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ObjectiveIn this study, we aimed to investigate the classification of symptomatic plaques by evaluating the models generated via two different approaches, a radiomics-based machine learning (ML) approach, and an end-to-end learning approach which utilized deep learning (DL) techniques with several representative model frameworks.MethodsWe collected high-resolution magnetic resonance imaging (HRMRI) data from 104 patients with carotid artery stenosis, who were diagnosed with either symptomatic plaques (SPs) or asymptomatic plaques (ASPs), in two medical centers. 74 patients were diagnosed with SPs and 30 patients were ASPs. Sampling Perfection with Application-optimized Contrasts (SPACE) by using different flip angle Evolutions was used for MRI imaging. Repeated stratified five-fold cross-validation was used to evaluate the accuracy and receiver operating characteristic (ROC) of the trained classifier. The two proposed approaches were investigated to train the models separately. The difference in the model performance of the two proposed methods was quantitatively evaluated to find a better model to differentiate between SPs and ASPs.Results3D-SE-Densenet-121 model showed the best performance among all prediction models (AUC, accuracy, precision, sensitivity, and F1-score of 0.9300, 0.9308, 0.9008, 0.8588, and 0.8614, respectively), which were 0.0689, 0.1119, 0.1043, 0.0805, and 0.1089 higher than the best radiomics-based ML model (MLP). Decision curve analysis showed that the 3D-SE-Densenet-121 model delivered more net benefit than the best radiomics-based ML model (MLP) with a wider threshold probability.ConclusionThe DL models were able to accurately differentiate between symptomatic and asymptomatic carotid plaques with limited data, which outperformed radiomics-based ML models in identifying symptomatic plaques.

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Section: Discussionsupporting

confidence: 91%

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques

Gui

Cao

Zhang

et al. 2023

Front. Cardiovasc. Med.

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“…The first study aimed to investigate quantitative differences between with region of deep venous with and without DVT by classifying the region of deep vein. This formative study indicated that the CNN model can extract useful features that can distinguish the region containing DVT from other regions 12 . However, the result of the study did not include information about localization of the DVT in LECTA.…”

Section: Introductionmentioning

confidence: 88%

“…Imaging modalities for DVT diagnosis include ultrasonography (US), computed tomography angiography of the bilateral lower extremities (LECTA), magnetic resonance imaging (MRI), and catheter venography. To overcome the limitations of the DVT manual analysis, studies have been conducted using various imaging modalities and have shown the potential and efficiency of an AI-based CAD system for DVT diagnosis 8 – 12 . Among the image modalities, LECTA was found to be more advantageous—it provided more objective images than US; it is easily accessible and frequently used to provide information about extravascular tissues in the bilateral lower extremities and abdominopelvic region.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach

Seo

Park

Kim

et al. 2023

Sci Rep

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Early diagnosis of deep venous thrombosis is essential for reducing complications, such as recurrent pulmonary embolism and venous thromboembolism. There are numerous studies on enhancing efficiency of computer-aided diagnosis, but clinical diagnostic approaches have never been considered. In this study, we evaluated the performance of an artificial intelligence (AI) algorithm in the detection of iliofemoral deep venous thrombosis on computed tomography angiography of the lower extremities to investigate the effectiveness of using the clinical approach during the feature extraction process of the AI algorithm. To investigate the effectiveness of the proposed method, we created synthesized images to consider practical diagnostic procedures and applied them to the convolutional neural network-based RetinaNet model. We compared and analyzed the performances based on the model’s backbone and data. The performance of the model was as follows: ResNet50: sensitivity = 0.843 (± 0.037), false positives per image = 0.608 (± 0.139); ResNet152 backbone: sensitivity = 0.839 (± 0.031), false positives per image = 0.503 (± 0.079). The results demonstrated the effectiveness of the suggested method in using computed tomography angiography of the lower extremities, and improving the reporting efficiency of the critical iliofemoral deep venous thrombosis cases.

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“…43,44 Similarly, ML-based tools have been developed for computer-aided diagnosis of DVT, although the majority utilize MR/CE-MRI or CT-venography, while the most widely employed diagnostic technique is compression ultrasound. [45][46][47][48] Aiming to equip non-specialists to detect DVT, a deep learning approach to compression ultrasound images was developed and externally validated with a negative predictive Bleeding, Thrombosis and Vascular Biology 2024; 3(s1):123 value of 98-99%. The authors also performed a cost analysis of integrating this ML tool into their current diagnostic pathway and estimated the net monetary benefits.…”

Section: Machine Learning Applications For Image Recognition In Venou...mentioning

confidence: 99%

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

Patell,

Zwicker,

Singh

et al. 2024

Bleed Thromb Vascul Biol

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The goal of machine learning (ML) is to create informative signals and useful tasks by leveraging large datasets to derive computational algorithms. ML has the potential to revolutionize the healthcare industry by boosting productivity, enhancing safe and effective patient care, and lightening the load on clinicians. In addition to gaining mechanistic insights into cancer-associated thrombosis (CAT), ML can be used to improve patient outcomes, streamline healthcare delivery, and spur innovation. Our review paper delves into the present and potential applications of this cutting-edge technology, encompassing three areas: i) computer vision-assisted diagnosis of thromboembolism from radiology data; ii) case detection from electronic health records using natural language processing; iii) algorithms for CAT prediction and risk stratification. The availability of large, well-annotated, high-quality datasets, overfitting, limited generalizability, the risk of propagating inherent bias, and a lack of transparency among patients and clinicians are among the challenges that must be overcome in order to effectively develop ML in the health sector. To guarantee that this powerful instrument can be utilized to maximize innovation in CAT, clinicians can collaborate with stakeholders such as computer scientists, regulatory bodies, and patient groups.

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Comparison between Deep Learning and Conventional Machine Learning in Classifying Iliofemoral Deep Venous Thrombosis upon CT Venography

Cited by 16 publications

References 23 publications

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques

Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

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