Smart scanning: automatic detection of superficially located lymph nodes using ultrasound – initial results

Abstract

Purpose

Over the last few years, there has been an increasing focus on integrating artificial intelligence (AI) into existing imaging systems. This also applies to ultrasound. There are already applications for thyroid and breast lesions that enable AI-assisted sonography directly on the device. However, this is not yet the case for lymph nodes.

Materials and Methods

The aim was to test whether already established programs for AI-assisted sonography of breast lesions and thyroid nodules are also suitable for identifying and measuring superficial lymph nodes. For this purpose, the two programs were used as a supplement to routine ultrasound examinations of superficial lymph nodes. The accuracy of detection by AI was then evaluated using a previously defined score. If available, a comparison was made with cross-sectional imaging.

Results

The programs that were used are able to adequately detect lymph nodes in the majority of cases (78.6%). Problems were caused in particular by a high proportion of echo-rich fat, blurred differentiation from the surrounding tissues and the occurrence of lymph node conglomerates. The available cross-sectional images did not contradict the classification of the lesion as a lymph node in any case.

Conclusion

In the majority of cases, the tested programs are already able to detect and measure superficial lymph nodes. Further improvement can be expected through specific training of the software. Further developments and studies are required to assess risk of malignancy.

Key Points

• The inclusion of AI in imaging is increasingly becoming a scientific focus.

• The detection of lymph nodes is already possible using device-integrated AI software.

• Malignancy assessment of the detected lymph nodes is not yet possible.

Citation Format

• Rink M, Künzel J, Stroszczynski C et al. Smart scanning: automatic detection of superficially located lymph nodes using ultrasound – initial results. Fortschr Röntgenstr 2024; DOI 10.1055/a-2331-0951

Zusammenfassung

Hintergrund

In den letzten Jahren rückt die Integration künstlicher Intelligenz (KI) in bestehende Bildgebungen zunehmend in den Fokus. Dies gilt auch für die Sonografie. Für Läsionen der Schilddrüse sowie der Mamma existieren bereits Anwendungen, die unmittelbar am Gerät eine KI-assistierte Sonografie ermöglichen. Für Lymphknoten ist dies bisher nicht der Fall.

Material und Methoden

Getestet wurde, ob bereits etablierte Programme zur KI-assistierten Sonografie von Läsionen der Brust beziehungsweise Schilddrüsenknoten sich grundsätzlich auch dazu eignen, oberflächliche Lymphknoten zu erkennen und zu vermessen. Hierzu wurden die beiden Programme im Rahmen klinischer Routineuntersuchungen oberflächlicher Lymphknoten ergänzend zum Standard genutzt. Die Genauigkeit der Erfassung durch die KI wurde im Anschluss durch einen vorher definierten Score bewertet. Sofern verfügbar erfolgte ein Vergleich zu einer Schnittbildgebung.

Ergebnisse

Die genutzten Programme sind in der Mehrheit der Fälle (78,6%) in der Lage, Lymphknoten adäquat zu erfassen. Probleme bereiten insbesondere ein hoher Anteil echoreichen Fetts, eine unscharfe Abgrenzbarkeit zur Umgebung sowie das Auftreten von Lymphknoten-Konglomeraten. Die verfügbaren Schnittbildgebungen widersprachen in keinem Fall der Wertung der Läsion als Lymphknoten.

Schlussfolgerungen

Die getesteten Programme sind in der Mehrzahl der Fälle bereits in der Lage, oberflächliche Lymphknoten zu erfassen. Durch ein entsprechendes Training der Software ist eine weitere Verbesserung zu erwarten. Zur Dignitätseinschätzung sind weitere Entwicklungen und Studien notwendig.

Kernaussagen

• Die Einbeziehung von KI in Bildgebungen steht zunehmend im wissenschaftlichen Fokus.

• Die Erfassung oberflächlicher Lymphknoten ist durch eine geräteintegrierte KI-Software bereits möglich.

• Eine Dignitätsabschätzung der erfassten Lymphknoten ist durch diese Programme aktuell nicht möglich.

Keywords

artificial intelligence - ultrasound - multimodal ultrasound - lymphatic - lymph node ultrasound - technical aspects

Publication History

Received: 30 January 2024

Accepted after revision: 14 May 2024

Article published online:
17 June 2024

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