PurposeTo integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice.ApproachIn clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow. It automatically identified the ETT and checked its placement relative to the trachea and carina. The ETT overlay and misplacement alert messages generated by the AI system were compared with radiology reports as the reference. A survey study was also conducted to evaluate usefulness of the AI system in clinical practice.ResultsThe alert messages indicating that either the ETT was misplaced or not detected had a positive predictive value of 42% (21/50) and negative predictive value of 98% (161/164) based on the radiology reports. In the survey, radiologist and ICU physician users indicated that they agreed with the AI outputs and that they were useful.ConclusionsThe AI system performance in real-world clinical use was comparable to that seen in previous experiments. Based on this and physician survey results, the system can be deployed more widely at our institution, using insights gained from this evaluation to make further algorithm improvements and quality assurance of the AI system.
Lobectomy is a common and effective procedure for treating early-stage lung cancers. However, for patients with compromised pulmonary function (e.g. COPD) lobectomy can lead to major postoperative pulmonary complications. A technique for quantitatively predicting postoperative pulmonary function is needed to assist surgeons in assessing candidate’s suitability for lobectomy. We present a framework for quantitatively predicting the postoperative lung physiology and function using a combination of lung biomechanical modeling and machine learning strategies. A set of 10 patients undergoing lobectomy was used for this purpose. The image input consists of pre- and post-operative breath hold CTs. An automated lobe segmentation algorithm and lobectomy simulation framework was developed using a Constrained Adversarial Generative Networks approach. Using the segmented lobes, a patient-specific GPU-based linear elastic biomechanical and airflow model and surgery simulation was then assembled that quantitatively predicted the lung deformation during the forced expiration maneuver. The lobe in context was then removed by simulating a volume reduction and computing the elastic stress on the surrounding residual lobes and the chest wall. Using the deformed lung anatomy that represents the post-operative lung geometry, the forced expiratory volume in 1 second (FEV1) (the amount of air exhaled by a patient in 1 second starting from maximum inhalation), and forced vital capacity (FVC) (the amount of air exhaled by force from maximum inhalation), were then modeled. Our results demonstrated that the proposed approach quantitatively predicted the postoperative lobe-wise lung function at the FEV1 and FEV/FVC.
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