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Objectives <p>This study developed and validated a deep learning model for diagnosing lymphadenopathy (LA) using B-mode ultrasound (BUS) and color Doppler flow imaging (CDFI) videos.</p> Materials and methods <p>A retrospective and prospective study was conducted from January 2016 to August 2025, including 7371 patients (3824 male [51.9%], 3547 females [48.1%], median age, 52 years [9–94 years]) who underwent multimodal ultrasound examinations across six centers from five regions of China. A total of 147,420 key frames were extracted from BUS and CDFI videos of all patients for model training and validation. Besides, patient clinical information was integrated to enhance the diagnostic performance of the model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and precision (PRE). The clinical practical value of the model was verified by comparing with the performance of independent diagnosis and model-assisted diagnosis of radiologists with different levels of experience.</p> Results <p>This model achieved AUCs of 0.956 (95% CI: 0.925–0.981), 0.928 (95% CI: 0.884–0.965), and 0.912 (95% CI: 0.863–0.952) in the internal, retrospective external, and prospective external validation cohorts, respectively. In the retrospective external cohort, the average AUC of junior radiologists improved from 0.739 (95% CI: 0.676–0.801) to 0.891 (95% CI: 0.846–0.940) with the assistance of the model. In the prospective external cohort, their average AUC improved from 0.767 (95% CI: 0.705–0.829) to 0.899 (95% CI: 0.853–0.944).</p> Conclusion <p>This multimodal video-based deep learning model enhances LA diagnostic accuracy and shows strong potential as a noninvasive, efficient tool for clinical decision-making.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis><i> Why is multimodal ultrasound video needed in clinical practice to enhance the automated assessment of LA?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis><i> The proposed multimodal ultrasound video AI model achieved high diagnostic accuracy and robustness, outperforming senior radiologists in distinguishing benign from malignant LA across multicenter datasets</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis><i> This model offers a reliable, noninvasive clinical decision-support tool that enhances diagnostic performance, reduces operator dependence, and facilitates early detection and precise management of LA across healthcare institutions with varying resources</i>.</p> Graphical Abstract <p></p>

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A multimodal feature disentanglement model for lymphadenopathy diagnosis based on BUS and CDFI ultrasound videos: a retrospective, prospective, multicenter study

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • Ran Cao,
  • Yangyang Zhu,
  • Haina Zhao,
  • Zhibin Zhu,
  • Lin Chen,
  • Yue Hu,
  • Fei Ouyang,
  • Nannan Zhao,
  • Tao Jiang,
  • Yifang Li,
  • Wenyu Xing,
  • Juan Song,
  • Fang Nie,
  • Li Qiu,
  • Dean Ta

摘要

Objectives

This study developed and validated a deep learning model for diagnosing lymphadenopathy (LA) using B-mode ultrasound (BUS) and color Doppler flow imaging (CDFI) videos.

Materials and methods

A retrospective and prospective study was conducted from January 2016 to August 2025, including 7371 patients (3824 male [51.9%], 3547 females [48.1%], median age, 52 years [9–94 years]) who underwent multimodal ultrasound examinations across six centers from five regions of China. A total of 147,420 key frames were extracted from BUS and CDFI videos of all patients for model training and validation. Besides, patient clinical information was integrated to enhance the diagnostic performance of the model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and precision (PRE). The clinical practical value of the model was verified by comparing with the performance of independent diagnosis and model-assisted diagnosis of radiologists with different levels of experience.

Results

This model achieved AUCs of 0.956 (95% CI: 0.925–0.981), 0.928 (95% CI: 0.884–0.965), and 0.912 (95% CI: 0.863–0.952) in the internal, retrospective external, and prospective external validation cohorts, respectively. In the retrospective external cohort, the average AUC of junior radiologists improved from 0.739 (95% CI: 0.676–0.801) to 0.891 (95% CI: 0.846–0.940) with the assistance of the model. In the prospective external cohort, their average AUC improved from 0.767 (95% CI: 0.705–0.829) to 0.899 (95% CI: 0.853–0.944).

Conclusion

This multimodal video-based deep learning model enhances LA diagnostic accuracy and shows strong potential as a noninvasive, efficient tool for clinical decision-making.

Key Points

Question Why is multimodal ultrasound video needed in clinical practice to enhance the automated assessment of LA?

Findings The proposed multimodal ultrasound video AI model achieved high diagnostic accuracy and robustness, outperforming senior radiologists in distinguishing benign from malignant LA across multicenter datasets.

Clinical relevance This model offers a reliable, noninvasive clinical decision-support tool that enhances diagnostic performance, reduces operator dependence, and facilitates early detection and precise management of LA across healthcare institutions with varying resources.

Graphical Abstract