Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated humanlevel performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.CIBERSAM of the Instituto de Salud Carlos III 495-2020UMA18-FEDERJA-084Autonomous Government Andalusia (Spain) RTX A6000 48NVIDIA Corporation 101057746Horizon Europe project PRE-ACTEuropean Commission Horizon Europe Program 22 00058Swiss State Secretariat for Education, Research and Innovation (SERI) 2020-0-01361Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea Ministry of Science & ICT (MSIT), Republic of KoreaArtificial Intelligence Graduate School Program (Yonsei University)Funding for open access charge: Universidad de Granada / CBU

Safety of hospital discharge before return of bowel function after elective colorectal surgery

Background: Ileus is common after colorectal surgery and is associated with an increased risk of postoperative complications. Identifying features of normal bowel recovery and the appropriateness for hospital discharge is challenging. This study explored the safety of hospital discharge before the return of bowel function.Methods: A prospective, multicentre cohort study was undertaken across an international collaborative network. Adult patients undergoing elective colorectal resection between January and April 2018 were included. The main outcome of interest was readmission to hospital within 30 days of surgery. The impact of discharge timing according to the return of bowel function was explored using multivariable regression analysis. Other outcomes were postoperative complications within 30 days of surgery, measured using the Clavien-Dindo classification system.Results: A total of 3288 patients were included in the analysis, of whom 301 (9.2 per cent) were discharged before the return of bowel function. The median duration of hospital stay for patients discharged before and after return of bowel function was 5 (i.q.r. 4-7) and 7 (6-8) days respectively (P < 0.001). There were no significant differences in rates of readmission between these groups (6.6 versus 8.0 per cent; P = 0.499), and this remained the case after multivariable adjustment for baseline differences (odds ratio 0.90, 95 per cent c.i. 0.55 to 1.46; P = 0.659). Rates of postoperative complications were also similar in those discharged before versus after return of bowel function (minor: 34.7 versus 39.5 per cent; major 3.3 versus 3.4 per cent; P = 0.110).Conclusion: Discharge before return of bowel function after elective colorectal surgery appears to be safe in appropriately selected patients

Timing of nasogastric tube insertion and the risk of postoperative pneumonia: an international, prospective cohort study

Aim: Aspiration is a common cause of pneumonia in patients with postoperative ileus. Insertion of a nasogastric tube (NGT) is often performed, but this can be distressing. The aim of this study was to determine whether the timing of NGT insertion after surgery (before versus after vomiting) was associated with reduced rates of pneumonia in patients undergoing elective colorectal surgery. Method: This was a preplanned secondary analysis of a multicentre, prospective cohort study. Patients undergoing elective colorectal surgery between January 2018 and April 2018 were eligible. Those receiving a NGT were divided into three groups, based on the timing of the insertion: routine NGT (inserted at the time of surgery), prophylactic NGT (inserted after surgery but before vomiting) and reactive NGT (inserted after surgery and after vomiting). The primary outcome was the development of pneumonia within 30 days of surgery, which was compared between the prophylactic and reactive NGT groups using multivariable regression analysis. Results: A total of 4715 patients were included in the analysis and 1536 (32.6%) received a NGT. These were classified as routine in 926 (60.3%), reactive in 461 (30.0%) and prophylactic in 149 (9.7%). Two hundred patients (4.2%) developed pneumonia (no NGT 2.7%; routine NGT 5.2%; reactive NGT 10.6%; prophylactic NGT 11.4%). After adjustment for confounding factors, no significant difference in pneumonia rates was detected between the prophylactic and reactive NGT groups (odds ratio 1.03, 95% CI 0.56–1.87, P = 0.932). Conclusion: In patients who required the insertion of a NGT after surgery, prophylactic insertion was not associated with fewer cases of pneumonia within 30 days of surgery compared with reactive insertion