Real-time Phonocardiogram Anomaly Detection by Adaptive 1D Convolu‐ tional Neural Networks

The heart sound signals (Phonocardiogram - PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnostic test in outpatient monitoring to assess heart hemodynamic status. The need for an automated and accurate anomaly detection method for PCG has thus become imminent. To determine the state-of-the-art PCG classification algorithm, 48 international teams competed in the PhysioNet (CinC) Challenge in 2016 over the largest benchmark dataset with 3126 records with the classification outputs, normal (N), abnormal (A) and unsure – too noisy (U). In this study, our aim is to push this frontier further; however, we focus deliberately on the anomaly detection problem while assuming a reasonably high Signal-to-Noise Ratio (SNR) on the records. By using 1D Convolutional Neural Networks trained with a novel data purification approach, we aim to achieve the highest detection performance and real-time processing ability with significantly lower delay and computational complexity. The experimental results over the high-quality subset of the same benchmark dataset show that the proposed approach achieves both objectives. Furthermore, our findings reveal the fact that further improvements indeed require a personalized (patient-specific) approach to avoid major drawbacks of a global PCG classification approach.publishedVersionPeer reviewe

Effects of Ramadan observance on repeated cycle ergometer sprinting and associated inflammatory and oxidative stress responses in trained young men

Aim: was to assess the effects of Ramadan observance upon repeated sprints and associated inflammatory and oxidative stress responses. Methods: Ten young trained boxers were tested during a control period (C), at the end of the first week (R-1), and during the fourth week of Ramadan observance (R-4). On each occasion, they performed three vertical jumps, 10 x 6 s repeated sprints on a cycle ergometer, followed by three final vertical jumps 1 min after. Surface electrodes measured the EMG activity of the vastus lateralis during jumps performed before and after sprinting. Oxidative stress (malondialdehyde, total antioxidant and catalase), inflammatory markers (C-reactive protein, Interleukin-6 and homocysteine), muscle damage (CPK and LDH) and blood glucose were measured at rest and after completing the exercise protocol. Results: The overall sprint performance was reduced at R-1 compared to C (-6.3 ± 1.2%, p = 0.025), but had recovered by R-4. Jump height decreased after the repeated sprints (

A case report of gastroduodenal artery pseudoaneurysm and giant pancreatic pseudocyst following acute pancreatitis revealed by obstructive jaundice

Acute necrotic pancreatitis is an emergency of evolution and is often unpredictable because of the potentially life-threatening complications it can cause. We report a unique case of a 56-year-old woman hospitalized for acute necrotic pancreatitis. The evolution of the latter was characterized by the occurrence of two very rare complications, of which the clinical presentations were atypical. The first complication was a gastroduodenal pseudoaneurysm compressing the main biliary tract and causing obstructive jaundice, which evolved well following percutaneous embolization. The second complication was a giant 20 cm pancreatic pseudocyst revealed by obstructive jaundice secondary to biliary compression, which progressed well following surgical treatment

Integrating artificial intelligence into lung cancer screening: a randomised controlled trial protocol

Introduction Lung cancer (LC) is the most common cause of cancer-related deaths worldwide. Its early detection can be achieved with a CT scan. Two large randomised trials proved the efficacy of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk populations. The decrease in specific mortality is 20%–25%.Nonetheless, implementing LCS on a large scale faces obstacles due to the low number of thoracic radiologists and CT scans available for the eligible population and the high frequency of false-positive screening results and the long period of indeterminacy of nodules that can reach up to 24 months, which is a source of prolonged anxiety and multiple costly examinations with possible side effects.Deep learning, an artificial intelligence solution has shown promising results in retrospective trials detecting lung nodules and characterising them. However, until now no prospective studies have demonstrated their importance in a real-life setting.Methods and analysis This open-label randomised controlled study focuses on LCS for patients aged 50–80 years, who smoked more than 20 pack-years, whether active or quit smoking less than 15 years ago. Its objective is to determine whether assisting a multidisciplinary team (MDT) with a 3D convolutional network-based analysis of screening chest CT scans accelerates the definitive classification of nodules into malignant or benign. 2722 patients will be included with the aim to demonstrate a 3-month reduction in the delay between lung nodule detection and its definitive classification into benign or malignant.Ethics and dissemination The sponsor of this study is the University Hospital of Nice. The study was approved for France by the ethical committee CPP (Comités de Protection des Personnes) Sud-Ouest et outre-mer III (No. 2022-A01543-40) and the Agence Nationale du Medicament et des produits de Santé (Ministry of Health) in December 2023. The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations.Trial registration number NCT05704920