Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation

Abstract

Accurate detection of pneumothorax on chest radiographs, the most common complication of percutaneous transthoracic needle biopsies (PTNBs), is not always easy in practice. A computer-aided detection (CAD) system may help detect pneumothorax.Purpose: To investigate whether a deep learning-based CAD system can improve detection performance for pneumothorax on chest radiographs after PTNB in clinical practice. Materials and Methods:A CAD system for post-PTNB pneumothorax detection on chest radiographs was implemented in an institution in February 2020. This retrospective cohort study consecutively included chest radiographs interpreted with CAD assistance (CAD-applied group; February 2020 to November 2020) and those interpreted before implementation (non-CAD group; January 2018 to January 2020). The reference standard was defined by consensus reading by two radiologists. The diagnostic accuracy for pneumothorax was compared between the two groups using generalized estimating equations. Matching was performed according to whether the radiograph reader and PTNB operator were the same using the greedy method.Results: A total of 676 radiographs from 655 patients (mean age: 67 years 6 11; 390 men) in the CAD-applied group and 676 radiographs from 664 patients (mean age: 66 years 6 12; 400 men) in the non-CAD group were included. The incidence of pneumothorax was 18.2% (123 of 676 radiographs) in the CAD-applied group and 22.5% (152 of 676 radiographs) in the non-CAD group (P = .05). The CAD-applied group showed higher sensitivity (85.4% vs 67.1%), negative predictive value (96.8% vs 91.3%), and accuracy (96.8% vs 92.3%) than the non-CAD group (all P , .001). The sensitivity for a small amount of pneumothorax improved in the CAD-applied group (pneumothorax of ,10%: 74.5% vs 51.4%, P = .009; pneumothorax of 10%-15%: 92.7% vs 70.2%, P = .008). Among patients with pneumothorax, 34 of 655 (5.0%) in the non-CAD group and 16 of 664 (2.4%) in the CAD-applied group (P = .009) required subsequent drainage catheter insertion. Conclusion:A deep learning-based computer-aided detection system improved the detection performance for pneumothorax on chest radiographs after lung biopsy.

Implementation of artificial intelligence in thoracic imaging—a what, how, and why guide from the European Society of Thoracic Imaging (ESTI)

“…If AI systems find pathology normally invisible to the naked eye, triggering further investigation which might cause additional radiation exposure and medicalisation, in direct contradiction with the “choose wisely” campaign aimed at reducing additional exams [ 70 ]. An example is the recent paper showing how AI improved the detection of pneumothorax after biopsies [ 71 ]. However, the clinical significance of missed pneumothoraxes was not discussed, as pneumothoraxes < 2 cm in clinically stable patients require no intervention, and the value of higher detection remains questionable [ 72 ].…”

Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial

“…However, the performance and efficacy (i.e., enhancing the accuracy of physicians’ interpretation) of AI-CAD have been evaluated primarily in retrospective, experimental settings, which cannot fully replicate the conditions of daily practice [ 12 13 14 15 16 17 20 ]. Although several studies have reported an increase in the accuracy of interpretation following the implementation of AI-CAD [ 21 22 ], the results of such “before-and-after” studies may be biased because of a lack of comparability between before and after implementation. Moreover, improving the accuracy of CR interpretation does not necessarily result in improvements in patient management, patient outcomes, or workflow efficiency [ 23 ].…”

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

“…CNN-based classifiers have proved highly accurate for image recognition 7 and have consequently impacted diagnostic imaging in the medical field. They have been applied to the detection of lung cancer from chest X-ray images 8 , determination of retinal detachment 9 , detection of osteoporosis 10 , screening of breast cancer 11 , etc. In addition, many deep learning-related studies have been reported in the field of dentistry, and classifiers have been developed for areas such as caries 12 , periapical lesions 13 , dental implants 14 , maxillary sinusitis 15 , and position classification of the mandibular third molars 16 .…”