Deep learning-based diagnosis from endobronchial ultrasonography images of pulmonary lesions

Abstract

Endobronchial ultrasonography with a guide sheath (EBUS-GS) improves the accuracy of bronchoscopy. The possibility of differentiating benign from malignant lesions based on EBUS findings may be useful in making the correct diagnosis. The convolutional neural network (CNN) model investigated whether benign or malignant (lung cancer) lesions could be predicted based on EBUS findings. This was an observational, single-center cohort study. Using medical records, patients were divided into benign and malignant groups. We acquired EBUS data for 213 participants. A total of 2,421,360 images were extracted from the learning dataset. We trained and externally validated a CNN algorithm to predict benign or malignant lung lesions. Test was performed using 26,674 images. The dataset was interpreted by four bronchoscopists. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the CNN model for distinguishing benign and malignant lesions were 83.4%, 95.3%, 53.6%, 83.8%, and 82.0%, respectively. For the four bronchoscopists, the accuracy rate was 68.4%, sensitivity was 80%, specificity was 39.6%, PPV was 76.8%, and NPV was 44.2%. The developed EBUS-computer-aided diagnosis system is expected to read EBUS findings that are difficult for clinicians to judge with precision and help differentiate between benign lesions and lung cancers.

Deep learning with test-time augmentation for radial endobronchial ultrasound image differentiation: a multicentre verification study

“…Hotta et al used EBUS data from 213 participants to train a CNN algorithm, which achieved accuracy of 83.4%, sensitivity of 95.3% and specificity of 53.6% in differentiating benign from malignant lung lesions. 29 Their results provide further support for our assertion that CNN models could be used to differentiate between benign and malignant lesions based on rEBUS images.…”

Development and validation of the artificial intelligence (AI)-based diagnostic model for bronchial lumen identification

“…Feng et al ( 30 ) used fluorescent bronchoscopy pictures of 12 cases of adenocarcinoma and 11 cases of squamous cell carcinoma as a data set, and linear regression machine learning methods for classification, and ultimately achieved an accuracy of 83% for lung cancer classification. Hotta et al ( 31 ) also trained CNN model to predict benign or malignant lesions based on endobronchial ultrasonography findings. These studies mainly focused on AI assisted diagnosis of bronchial lesions.…”

Artificial intelligence in interventional pulmonology

“…Chen et al [43] evaluated a CNN approach to predict malignancy in peripheral pulmonary lesions using RP-EBUS images, achieving high accuracy (85%) when combining CNN with transfer learning and support vector machine. Hotta et al also reported deep-learning-based CADx of peripheral pulmonary lesions using RP-EBUS images, achieving an accuracy of 83%, along with sensitivity (95%) and specificity (54%) [44 ▪ ]. In contrast, four bronchoscopists achieved lower accuracy (68%), sensitivity (80%), and specificity (40%).…”