Background: Thoracic artificial lungs have been proposed as a bridge to transplant in patients with end-stage lung disease. Systemic embolic complications can occur after thoracic artificial lung attachment in the pulmonary artery to left atrium configuration. Therefore, we evaluated the function of a compliant thoracic artificial lung attached via the proximal pulmonary artery to distal main pulmonary artery configuration.
Methods: The compliant thoracic artificial lung was attached to 5 sheep (63 ± 0.9 kg) in the proximal pulmonary artery to distal main pulmonary artery configuration. Device function and animal hemodynamics were assessed at baseline and with approximately 60%, 75%, and 90% of cardiac output diverted to the compliant thoracic artificial lung. At each condition, dobutamine (0 and 5 μg·kg(-1)·min(-1)) was used to simulate rest and exercise conditions.
Results: At rest, cardiac output decreased from 6.20 ± 0.53 L/min at baseline to 5.40 ± 0.43, 4.66 ± 0.31, and 4.05 ± 0.27 L/min with 60%, 75%, and 90% of cardiac output to the compliant thoracic artificial lung, respectively (P < .01 for each flow diversion vs baseline). During exercise, cardiac output decreased from 7.85 ± 0.70 L/min at baseline to 7.46 ± 0.55, 6.93 ± 0.51, and 5.96 ± 0.44 L/min (P = .82, P = .19, and P < .01 with respect to baseline) with 60%, 75%, and 90% of cardiac output to the compliant thoracic artificial lung, respectively. The artificial lung resistance averaged 0.46 ± 0.02 and did not vary significantly with blood flow rate.
Conclusions: Use of a compliant thoracic artificial lung may be feasible in the proximal pulmonary artery to distal main pulmonary artery setting if its blood flow is held at less than 75% of cardiac output. To ensure a decrease in cardiac output of less than 10%, a blood flow rate less than 60% of cardiac output is advised.
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http://dx.doi.org/10.1016/j.jtcvs.2013.01.020 | DOI Listing |
Purpose: This brief report aims to summarize and discuss the methodologies of eXplainable Artificial Intelligence (XAI) and their potential applications in surgery.
Methods: We briefly introduce explainability methods, including global and individual explanatory features, methods for imaging data and time series, as well as similarity classification, and unraveled rules and laws.
Results: Given the increasing interest in artificial intelligence within the surgical field, we emphasize the critical importance of transparency and interpretability in the outputs of applied models.
J Multidiscip Healthc
January 2025
School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Objective: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments.
View Article and Find Full Text PDFAdvances in personalized medicine and Systems Biology have introduced probabilistic models and error discovery to cardiovascular care, aiding disease prevention and procedural planning. However, clinical application faces cultural, technical, and methodological hurdles. Patient autonomy remains essential, with shared decision-making (SDM) gaining importance in managing complex cardiovascular treatment options.
View Article and Find Full Text PDFInt J Surg
January 2025
Carcinoma Department of Traditional Chinese Medicine, Dianjiang People's Hospital of Chongqing, Chongqing, PR China.
The widespread adoption of high-resolution computed tomography (CT) screening has led to increased detection of small pulmonary nodules, necessitating accurate localization techniques for surgical resection. This review examines the evolution, efficacy, and safety of various localization methods for small pulmonary nodules. Studies focusing on localization techniques for pulmonary nodules ≤30 mm in diameter were included, with emphasis on technical success rates and complication profiles.
View Article and Find Full Text PDFRadiol Med
January 2025
Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Purpose: To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.
Methods: On lateral full-spine radiographs of 295 consecutive patients, a two-staged region-based convolutional neural network (R-CNN) was trained to detect anatomical landmarks and calculate thoracic kyphosis (TK), lumbar lordosis (LL), sacral slope (SS), and sagittal vertical axis (SVA). Performance was evaluated on 65 radiographs not used for training, which were measured independently by 6 readers (3 radiologists, 3 surgeons), and the median per measurement was set as the reference standard.
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