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Updated: Jan 9, 2026
Author Spotlight: Expanding Interventional Pulmonology Research with Robotic-Assisted Bronchoscopy
Published on: July 19, 2024
Efficacy of a virtual bronchoscopic navigation system improved by deep learning for biopsy of peripheral lung
Jisong Zhang1, Ding Wang1, Nanyu Li2
1Department of Pulmonary and Critical Care Medicine, Regional Medical Center for National Institute of Respiratory Disease, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Shangcheng District, Hangzhou, China.
Background:
Existing virtual bronchoscopic navigation (VBN) biopsy systems are ineffective in reconstructing small airway trees of 2-3 mm, which leads to an inability to accurately guide the biopsy of peripheral pulmonary lesions (PPL). This study intended to compare the diagnostic rate of PPL by the modified SARS-pro (small airway reconstruction system-pro) system and the original VBN system.
Methods:
This single-center randomized controlled trial enrolled subjects aged ≥18 years who had one or more PPLs between August 2023 and December 2024 at a hospital. Subjects were randomly assigned to the SARS-pro system and the VBN system (1:1). The outcomes were the diagnostic positive rate of PPL and adverse events in subjects. The diagnosis rate outcomes were evaluated in the per-protocol set (PPS).
Results:
Ninety-five eligible subjects were recruited, 95 subjects were included in the full analysis set (FAS), and 92 subjects were included in PPS. The SARS-pro system exhibited a higher positive diagnostic rate for PPL than the VBN system on FAS [43 (91.49%) vs. 30 (62.50%), P = 0.002] and PPS [43 (93.48%) vs. 30 (65.22%), P = 0.002]. Furthermore, the positive diagnostic rate of PPL was significantly higher for the SARS-pro system than for the VBN system in some characteristic subgroups, such as females, no smoking history, lesion size of 1-2 cm, and no bronchial signs.
Conclusion:
The improved SARS-pro system presented a higher diagnostic rate for PPL than the VBN system, which may suggest the application of deep learning technology in improving the diagnostic rate of lung biopsy.
