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Zolotukhin I.A.

Pirogov Russian National Research Medical University

Kvasnikov B.B.

JSC «Servier»

Linnik O.Zh.

JSC «Servier»

Shiyakhmetov S.B.

Pirogov Russian National Research Medical University

Butova K.G.

Pirogov Russian National Research Medical University

Artificial Intelligence Based Application Accuracy in Diagnostics of C1-C2 Chronic Venous Disease

Authors:

Zolotukhin I.A., Kvasnikov B.B., Linnik O.Zh., Shiyakhmetov S.B., Butova K.G.

More about the authors

Journal: Journal of Venous Disorders. 2024;18(2): 132‑138

DOI: 10.17116/flebo202418021132

Views: 569

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To cite this article:

Zolotukhin IA, Kvasnikov BB, Linnik OZh, Shiyakhmetov SB, Butova KG. Artificial Intelligence Based Application Accuracy in Diagnostics of C1-C2 Chronic Venous Disease. Journal of Venous Disorders. 2024;18(2):132‑138. (In Russ.)
https://doi.org/10.17116/flebo202418021132

Подписка 2025 (Journal of Venous Disorders)
 
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Artificial Intelligence based (AI) technologies have become one of the most exciting areas of medical researches. AI based applications have demonstrated high accuracy in identifying of venous diseases.

OBJECTIVE

To evaluate the accuracy of the AIVARIX application in identifying symptoms of C1-C2 chronic venous disease (CVD) via analysis of images of lower extremities.

MATERIAL AND METHODS

A multiple-center prospective study was conducted in routine clinical practice in Russia. We included outpatients with symptoms of CVD. Sensitivity (Sn) and specificity (Sp) of the application for detecting CVD CEAP C1-C2 were assessed. After examination and diagnosis, phlebologist took images of the reference area of the lower extremities. These images were analyzed by the application for identifying C1-C2 symptoms. We compared the reports of phlebologists and applications, as well as analyzed Sn and Sp. The study is registered on clinicaltrials.gov (NCT05612217).

RESULTS

The study included 433 patients (332 women, 76.7%) aged 46.0 years. Mean body mass index was 25.5 kg/m2. CVD class C0(s), C1, C2, C3-6 and abnormalities not associated with CVD were diagnosed in 30.5% (n=132), 29.8% (n=129), 21.2% (n=92), 9.7% (n=42) and 8.8% (n=38) of patients, respectively. For class C1, the app showed Sn and Sp of 75.2% (95% CI 66.8—82.4) and 86.5% (95% CI 82.2—90.1), for class C2 — 93.5% (95% CI 86.3—97.6) and 82.7% (95% CI 78.3—86.6), respectively.

CONCLUSION

Sensitivity and specificity of the AIVARIX application justify the use of this tool to detect symptoms of CVD class C1 and C2.

Keywords:

chronic venous diseases
symptom detection
artificial intelligence
telangiectasia
varicose veins

Authors:

Zolotukhin I.A.

Pirogov Russian National Research Medical University

Kvasnikov B.B.

JSC «Servier»

Linnik O.Zh.

JSC «Servier»

Shiyakhmetov S.B.

Pirogov Russian National Research Medical University

Butova K.G.

Pirogov Russian National Research Medical University

Received:

11.04.2024

Accepted:

24.04.2024

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