Location of Ruptured Bullae in Secondary Spontaneous Pneumothorax

Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application

Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard (p = 0.11) but inferior to the true label (difference in MAE: 3.03%). The amount of pneumothorax in thoracostomy patients was 21.6% in predicted cases and 18.5% in true cases.

Effect of overhang and stiffness on accessibility of catheter tip to lung defects under surgical constraints

Background

Steering a catheter tip to a defective spot on a lung during fibrin glue application under pleurography (FGAP) constitutes a very challenging task. Accurate control of tip position/slope of steerable catheters is critical for providing glue to air-leaking defects on lung during FGAP. Steerable catheters are composed of multiple segments with different flexural rigidities and experience nonlinear deformation. Therefore, predicting tip position with accuracy is difficult. The purpose of this paper is to present a novel method that can accurately control the tip of the catheter, and thus enhance its target accessibility.

Methods

The tip position of a deflected steerable catheter can be accurately predicted using a simulation tool depicting the curvature of a bent catheter based on a mechanics of materials analysis. The proposed method utilizes the simulation tool to enhance the target accessibility of the catheter’s tip. This assists medical doctors to not only select the optimal catheter prior to operation, but also to steer the catheter to the defects effectively. Simulation reliability is tested by performing a catheter deflection experiment and measuring the tip position.

Results

The simulation result accurately predicts the location of the catheter tip with an error of less than 5 mm to the experimental results.

Conclusions

Controlling pull-wire tensions and overhang lengths based on simulation results substantially improves the target accessibility of the catheter tip.