Real-time carotid plaque recognition from dynamic ultrasound videos based on artificial neural network

Abstract

Purpose Carotid ultrasound allows noninvasive assessment of vascular anatomy and function with real-time display. Based on the transfer learning method, a series of research results have been obtained on the optimal image recognition and analysis of static images. However, for carotid plaque recognition, there are high requirements for self-developed algorithms in real-time ultrasound detection. This study aims to establish an automatic recognition system, Be Easy to Use (BETU), for the real-time and synchronous diagnosis of carotid plaque from ultrasound videos based on an artificial neural network.

Materials and Methods 445 participants (mean age, 54.6±7.8 years; 227 men) were evaluated. Radiologists labeled a total of 3259 segmented ultrasound images from 445 videos with the diagnosis of carotid plaque, 2725 images were collected as a training dataset, and 554 images as a testing dataset. The automatic plaque recognition system BETU was established based on an artificial neural network, and remote application on a 5G environment was performed to test its diagnostic performance.

Results The diagnostic accuracy of BETU (98.5%) was consistent with the radiologist’s (Kappa = 0.967, P < 0.001). Remote diagnostic feedback based on BETU-processed ultrasound videos could be obtained in 150ms across a distance of 1023 km between the ultrasound/BETU station and the consultation workstation.

Conclusion Based on the good performance of BETU in real-time plaque recognition from ultrasound videos, 5G plus Artificial intelligence (AI)-assisted ultrasound real-time carotid plaque screening was achieved, and the diagnosis was made.

Zusammenfassung

Hintergrund Der Karotis-Ultraschall ermöglicht eine nicht invasive Beurteilung der Anatomie und Funktion von Gefäßen in Echtzeit. Auf der Grundlage der Transfer-Learning-Methode wurden viele Forschungsergebnisse zur optimalen Bilderkennung und Analyse statischer Bilder gewonnen. Für die Erkennung von Karotisplaques bestehen jedoch hohe Anforderungen an selbstentwickelte Algorithmen für die Echtzeit-Ultraschall-Erkennung. Ziel der Studie ist es, ein automatisches Erkennungssystem – Be-Easy-to-Use (BETU) – für die Echtzeit- und Synchrondiagnose von Karotisplaques aus Ultraschallvideos auf Basis eines künstlichen neuronalen Netzes zu entwickeln.

Zu Material und Methoden 445 Teilnehmer (Durchschnittsalter: 54,6 ±7,8 Jahre; 227 davon Männer) wurden untersucht. Radiologen stellten bei insgesamt 3259 segmentierten Ultraschallbildern aus 445 Videos die Diagnose „Karotisplaque“; 2725 Bilder wurden als Trainingsdatensatz und 554 Bilder als Testdatensatz gesammelt. Das automatische Plaque-Erkennungssystem BETU wurde auf Basis eines künstlichen neuronalen Netzes etabliert, und dessen diagnostische Leistung wurde durch eine Remote-Anwendung in einer 5G-Umgebung getestet.

Ergebnisse Die diagnostische Genauigkeit von BETU (98,5%) stimmte mit der des Radiologen überein (Kappa = 0,967; p < 0,001). Ein auf den BETU-prozessierten Ultraschallvideos basierendes Ferndiagnose-Feedback konnte in 150 ms über eine Entfernung von 1023 km zwischen dem Ultraschall-/BETU-System und dem Konsultations-Bildschirm erhalten werden.

Schlussfolgerung Basierend auf der guten Leistung von BETU bei der Echtzeit-Plaque-Erkennung aus Ultraschallvideos wurde ein 5G- plus durch künstliche Intelligenz (KI) gestütztes Echtzeit-Ultraschall-Screening von Karotisplaques durchgeführt und die Diagnose gestellt.

Keywords

Artificial intelligence - Carotid plaque - METHODS & TECHNIQUES - ultrasound - YOLOv4 neural network

Publikationsverlauf

Eingereicht: 06. Juni 2023

Angenommen nach Revision: 15. September 2023

Artikel online veröffentlicht:
19. Dezember 2023

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