Correlation of Imaging Characteristics of Carotid Plaque with Clinical and Histopathological Features and Application of Artificial Intelligence Techniques in Identifying Carotid Disease
Introduction: Stroke is the third-leading cause of death. Carotid artery stenosis (CAS) accounts for 8%–37% of strokes. Landmark carotid trials have shown carotid endarterectomy (CEA) superior to best medical therapy (BMT) for symptomatic moderate stenosis and asymptomatic high-grade stenosis. This...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2025-04-01
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| Series: | Indian Journal of Vascular and Endovascular Surgery |
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| Online Access: | https://journals.lww.com/10.4103/ijves.ijves_123_24 |
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| author | G. Satyendra Ramnadh Pritee Sharma Vamsi Krishna Yerramsetty Y. V. Satish Kumar Rahul Agarwal Prem Chand Gupta |
| author_facet | G. Satyendra Ramnadh Pritee Sharma Vamsi Krishna Yerramsetty Y. V. Satish Kumar Rahul Agarwal Prem Chand Gupta |
| author_sort | G. Satyendra Ramnadh |
| collection | DOAJ |
| description | Introduction:
Stroke is the third-leading cause of death. Carotid artery stenosis (CAS) accounts for 8%–37% of strokes. Landmark carotid trials have shown carotid endarterectomy (CEA) superior to best medical therapy (BMT) for symptomatic moderate stenosis and asymptomatic high-grade stenosis. This study aims to correlate imaging characteristics of vulnerable plaque (VP) with clinical and histopathological features and to assess the ability of artificial intelligence (AI) techniques to identify VP and disease burden using duplex ultrasound (DU) data.
Materials and Methods:
This prospective study from August 2022 to April 2024 used DU and computerized tomographic angiography (CTA) or magnetic resonance angiography (MRA) to assess carotid plaques in patients planned for CEA and subjected plaques to histology. We also imaged carotid plaques using DU in asymptomatic patients on BMT. YOLOv8 deep learning and U-Net AI models were used to analyze ultrasound (US) images.
Results:
One hundred and fiftyOne hundred and fifty patients were included in the study. Sixty (40%) were symptomatic. The age range was 33–84 years (mean 58.9). One hundred and fifteen (76.7%) were males. Hypertension, diabetes, dyslipidemia, and smoking were seen in 59%, 56%, 49%, and 40%, respectively and were more prevalent in patients with VP (90%, 60%, 70%, and 50%). Of the 70 patients who underwent CEA, 60 were symptomatic and 10 asymptomatic. 70% of symptomatic patients had a RANKIN (mRS) score of 2–3. All surgical patients underwent DU, 49 CTA, and 21 MRA. Based on the US, 53/60 (88%) symptomatic patients had VP. 56/60 (93%) were confirmed VP on histology. Positive predictive value (PPV) for VP was highest for DU (95%), followed by MRA (89%) and CTA (73%). PPV of DU screening in operated asymptomatic patients was 100%. AI showed 96% PPV for identifying >50% stenosis. Model training in identifying VP accuracy increased from 0.73 to 0.94 metrics.
Conclusion:
DU is an excellent modality to identify the nature of carotid plaque and can be a good tool to identify VP in asymptomatic CAS. More data will improve AI accuracy. |
| format | Article |
| id | doaj-art-64744dfca75146d2af6f6c49094c2f11 |
| institution | DOAJ |
| issn | 0972-0820 2394-0999 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Indian Journal of Vascular and Endovascular Surgery |
| spelling | doaj-art-64744dfca75146d2af6f6c49094c2f112025-08-20T03:07:32ZengWolters Kluwer Medknow PublicationsIndian Journal of Vascular and Endovascular Surgery0972-08202394-09992025-04-0112212212710.4103/ijves.ijves_123_24Correlation of Imaging Characteristics of Carotid Plaque with Clinical and Histopathological Features and Application of Artificial Intelligence Techniques in Identifying Carotid DiseaseG. Satyendra RamnadhPritee SharmaVamsi Krishna YerramsettyY. V. Satish KumarRahul AgarwalPrem Chand GuptaIntroduction: Stroke is the third-leading cause of death. Carotid artery stenosis (CAS) accounts for 8%–37% of strokes. Landmark carotid trials have shown carotid endarterectomy (CEA) superior to best medical therapy (BMT) for symptomatic moderate stenosis and asymptomatic high-grade stenosis. This study aims to correlate imaging characteristics of vulnerable plaque (VP) with clinical and histopathological features and to assess the ability of artificial intelligence (AI) techniques to identify VP and disease burden using duplex ultrasound (DU) data. Materials and Methods: This prospective study from August 2022 to April 2024 used DU and computerized tomographic angiography (CTA) or magnetic resonance angiography (MRA) to assess carotid plaques in patients planned for CEA and subjected plaques to histology. We also imaged carotid plaques using DU in asymptomatic patients on BMT. YOLOv8 deep learning and U-Net AI models were used to analyze ultrasound (US) images. Results: One hundred and fiftyOne hundred and fifty patients were included in the study. Sixty (40%) were symptomatic. The age range was 33–84 years (mean 58.9). One hundred and fifteen (76.7%) were males. Hypertension, diabetes, dyslipidemia, and smoking were seen in 59%, 56%, 49%, and 40%, respectively and were more prevalent in patients with VP (90%, 60%, 70%, and 50%). Of the 70 patients who underwent CEA, 60 were symptomatic and 10 asymptomatic. 70% of symptomatic patients had a RANKIN (mRS) score of 2–3. All surgical patients underwent DU, 49 CTA, and 21 MRA. Based on the US, 53/60 (88%) symptomatic patients had VP. 56/60 (93%) were confirmed VP on histology. Positive predictive value (PPV) for VP was highest for DU (95%), followed by MRA (89%) and CTA (73%). PPV of DU screening in operated asymptomatic patients was 100%. AI showed 96% PPV for identifying >50% stenosis. Model training in identifying VP accuracy increased from 0.73 to 0.94 metrics. Conclusion: DU is an excellent modality to identify the nature of carotid plaque and can be a good tool to identify VP in asymptomatic CAS. More data will improve AI accuracy.https://journals.lww.com/10.4103/ijves.ijves_123_24american heart associationbest medical treatmentcarotid artery stentingcarotid endarterectomycomputed tomography angiographyintra plaque hemorrhagelipid rich necrotic coremagnetic resonance angiographyperipheral arterial diseasetransient ischaemic attack |
| spellingShingle | G. Satyendra Ramnadh Pritee Sharma Vamsi Krishna Yerramsetty Y. V. Satish Kumar Rahul Agarwal Prem Chand Gupta Correlation of Imaging Characteristics of Carotid Plaque with Clinical and Histopathological Features and Application of Artificial Intelligence Techniques in Identifying Carotid Disease Indian Journal of Vascular and Endovascular Surgery american heart association best medical treatment carotid artery stenting carotid endarterectomy computed tomography angiography intra plaque hemorrhage lipid rich necrotic core magnetic resonance angiography peripheral arterial disease transient ischaemic attack |
| title | Correlation of Imaging Characteristics of Carotid Plaque with Clinical and Histopathological Features and Application of Artificial Intelligence Techniques in Identifying Carotid Disease |
| title_full | Correlation of Imaging Characteristics of Carotid Plaque with Clinical and Histopathological Features and Application of Artificial Intelligence Techniques in Identifying Carotid Disease |
| title_fullStr | Correlation of Imaging Characteristics of Carotid Plaque with Clinical and Histopathological Features and Application of Artificial Intelligence Techniques in Identifying Carotid Disease |
| title_full_unstemmed | Correlation of Imaging Characteristics of Carotid Plaque with Clinical and Histopathological Features and Application of Artificial Intelligence Techniques in Identifying Carotid Disease |
| title_short | Correlation of Imaging Characteristics of Carotid Plaque with Clinical and Histopathological Features and Application of Artificial Intelligence Techniques in Identifying Carotid Disease |
| title_sort | correlation of imaging characteristics of carotid plaque with clinical and histopathological features and application of artificial intelligence techniques in identifying carotid disease |
| topic | american heart association best medical treatment carotid artery stenting carotid endarterectomy computed tomography angiography intra plaque hemorrhage lipid rich necrotic core magnetic resonance angiography peripheral arterial disease transient ischaemic attack |
| url | https://journals.lww.com/10.4103/ijves.ijves_123_24 |
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