This study aims to determine if a deep neural artificial intelligence (AI) network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by endobronchial ultrasound transbronchial needle aspiration(EBUS-TBNA), using the technique of segmentation. Images will be created from 300 lymph nodes videos from a prospective library and will be used as a derivation set to develop the algorithm. An additional100 lymph node images will be prospectively collected to validate if NeuralSeg can correctly apply the score.
Study Type
OBSERVATIONAL
Enrollment
52
All patients will undergo EBUS-TBNA as per routine care, except for the one difference where the procedures will be video-recorded so that they can be used for computer analysis at a later time. Static images will be obtained from EBUS videos in order to perform segmentation. Segmentation will be conducted by both an experienced endoscopist and NeuralSeg.
St. Joseph's Healthcare Hamilton
Hamilton, Ontario, Canada
Development of computer algorithm to identify lymph node ultrasonographic features
Objective: to determine whether a deep neural AI network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by EBUS, using the technique of segmentation on an existing (derivation) set of lymph node videos
Time frame: From retrospective data collection to algorithm development (1 month)
Validation of computer algorithm to identify lymph node ultrasonographic features
Objective: to determine whether NeuralSeg can correctly apply the Canada Lymph Node Score to a new (validation) set of lymph node videos that it has never seen before
Time frame: From prospective data collection to algorithm validation (6 months)
Accuracy and reliability of the segmentation performed by NeuralSeg
Objective: to compare the accuracy and reliability of the segmentation performed by NeuralSeg to the segmentation performed by an experienced endoscopic surgeon using DICE-SORENSEN coefficients.
Time frame: From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)
NeuralSeg prediction of lymph node malignancy
Objective: to determine whether NeuralSeg can accurately predict malignancy in lymph node when compared to biopsy results of the lymph node that was examined.
Time frame: From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)
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