Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques

Farhad, Arefinia; Reza, Rabiei; Azamossadat, Hosseini; Ali, Ghaemian; Arash, Roshanpoor; Mehrad, Aria; Zahra, Khorrami

doi:10.1186/s12872-023-03447-w

Cited by 7 publications

(2 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various AI methods, including deep learning-based methods, machine learning-based methods, and a combination of them, have been used along with different imaging tools such as CCTV, OCT, XCA, and IVUS. Table 1 shows the studies conducted in this field 39 .…”

Section: Related Workmentioning

confidence: 99%

Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images

Arefinia,

Aria,

Rabiei

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This study aimed to design an end-to-end deep learning model for estimating the value of fractional flow reserve (FFR) using angiography images to classify left anterior descending (LAD) branch angiography images with average stenosis between 50 and 70% into two categories: FFR > 80 and FFR ≤ 80. In this study 3625 images were extracted from 41 patients’ angiography films. Nine pre-trained convolutional neural networks (CNN), including DenseNet121, InceptionResNetV2, VGG16, VGG19, ResNet50V2, Xception, MobileNetV3Large, DenseNet201, and DenseNet169, were used to extract the features of images. DenseNet169 indicated higher performance compared to other networks. AUC, Accuracy, Sensitivity, Specificity, Precision, and F1-score of the proposed DenseNet169 network were 0.81, 0.81, 0.86, 0.75, 0.82, and 0.84, respectively. The deep learning-based method proposed in this study can non-invasively and consistently estimate FFR from angiographic images, offering significant clinical potential for diagnosing and treating coronary artery disease by combining anatomical and physiological parameters.

show abstract

Section: Related Workmentioning

confidence: 99%

Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images

Arefinia,

Aria,

Rabiei

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…These two articles focus on dental health and its connection to overall wellbeing. Moreover, Arefinia et al [40] provides a systematic literature review of techniques used in artificial intelligence to estimate fractional flow reserve, which is an important indicator of cardiovascular health. This article represents the application of technology in medicine and highlights the potential of artificial intelligence in improving diagnosis and treatment.…”

Section: A Review Of Biology Researchmentioning

confidence: 99%

Oral Cancer and HPV: Review Article

Pirmoradi,

Nazari,

Shafiee

et al. 2024

Asian Pac J Cancer Biol

View full text Add to dashboard Cite

Overview: Oral cancer poses a significant global health challenge, with approximately 300,000 new cases reported annually. Human papillomavirus (HPV) has emerged as a major risk factor for the development of oropharyngeal cancer, constituting around 60% of all oral cancer cases. This review aims to present a comprehensive overview of the current relationship between HPV and oral cancer, covering aspects such as epidemiology, clinical features, detection, treatment, and prevention strategies. Methods: A comprehensive literature search was conducted using Web of Science, PubMed, and Scopus databases to identify studies published in English from January 1983 to the present that investigated the association between HPV and oral cancer. Relevant articles were selected based on their applicability to the topic and methodological quality. Results: The literature search yielded 115 studies that met our inclusion criteria. Our analysis revealed that HPV is a common finding in oral cancer, with a pooled prevalence rate of 72.8%. HPV-positive oral cancer is associated with younger age at diagnosis, male gender, lower stage at presentation, and better prognosis compared to HPV-negative tumors. The HPV genotype most commonly detected in oral cancer is HPV-16, followed by HPV-18. Conclusion: HPV significantly contributes to the onset of oropharyngeal cancer, and identifying it early can serve as a valuable indicator for diagnosis and prognosis. More investigation is required to grasp the molecular mechanisms involved in HPV-related oral cancer and to formulate successful prevention and treatment approaches. Immunization against HPV, particularly in young males, shows potential for decreasing the occurrence of oral cancer linked to HPV.

show abstract

Artificial Intelligence for Identification of Images with Active Bleeding in Mesenteric and Celiac Arteries Angiography

Barash,

Livne,

Klang

et al. 2024

Cardiovasc Intervent Radiol

View full text Add to dashboard Cite

Purpose The purpose of this study is to evaluate the efficacy of an artificial intelligence (AI) model designed to identify active bleeding in digital subtraction angiography images for upper gastrointestinal bleeding. Methods Angiographic images were retrospectively collected from mesenteric and celiac artery embolization procedures performed between 2018 and 2022. This dataset included images showing both active bleeding and non-bleeding phases from the same patients. The images were labeled as normal versus images that contain active bleeding. A convolutional neural network was trained and validated to automatically classify the images. Algorithm performance was tested in terms of area under the curve, accuracy, sensitivity, specificity, F1 score, positive and negative predictive value. Results The dataset included 587 pre-labeled images from 142 patients. Of these, 302 were labeled as normal angiogram and 285 as containing active bleeding. The model’s performance on the validation cohort was area under the curve 85.0 ± 10.9% (standard deviation) and average classification accuracy 77.43 ± 4.9%. For Youden’s index cutoff, sensitivity and specificity were 85.4 ± 9.4% and 81.2 ± 8.6%, respectively. Conclusion In this study, we explored the application of AI in mesenteric and celiac artery angiography for detecting active bleeding. The results of this study show the potential of an AI-based algorithm to accurately classify images with active bleeding. Further studies using a larger dataset are needed to improve accuracy and allow segmentation of the bleeding. Graphical abstract

show abstract

Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques

Cited by 7 publications

References 86 publications

Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images

Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images

Oral Cancer and HPV: Review Article

Artificial Intelligence for Identification of Images with Active Bleeding in Mesenteric and Celiac Arteries Angiography

Contact Info

Product

Resources

About