Evolutionary Computation Paradigm to Determine Deep Neural Networks Architectures

Image classification is usually done using deep learning algorithms. Deep learning architectures are set deterministically. The aim of this paper is to propose an evolutionary computation paradigm that optimises a deep learning neural network’s architecture. A set of chromosomes are randomly generated, after which selection, recombination, and mutation are applied. At each generation the fittest chromosomes are kept. The best chromosome from the last generation determines the deep learning architecture. We have tested our method on a second trimester fetal morphology database. The proposed model is statistically compared with DenseNet201 and ResNet50, proving its competitiveness

Implementation of a Customized Safety Checklist in Gastrointestinal Endoscopy and the Importance of Team Time Out—A Dual-Center Pilot Study

Background and Objectives: Checking and correctly preparing the patient for endoscopic procedures is a mandatory step for the safety and quality of the interventions. The aim of this paper is to emphasize the importance and necessity of a “team time out” as well as the implementation of a customized “checklist” before the actual procedure. Material and Methods: We developed and implemented a checklist for the safe conduct of endoscopies and for the entire team to thoroughly know about the patient’s medical history. The subjects of this study were 15 physicians and 8 endoscopy nurses who performed overall 572 consecutive GI endoscopic procedures during the study period. Results: This is a prospective pilot study performed in the endoscopy unit of two tertiary referral medical centers. We customized a safety checklist that includes the steps to be followed before, during and after the examination. It brings together the whole team participating in the procedure in order to check the key points during the following three vital phases: before the patient falls asleep, before the endoscope is inserted and before the team leaves the examination room. The perception of team communication and teamwork was improved after the introduction of the checklist. The checklist completion rates, identity verification rates of patients by the endoscopist, adequate histological labeling management and explicit recording of follow-up recommendations are some of the parameters that improved post-intervention. Conclusions: Using a checklist and adapting it to local conditions is a high-level recommendation of the Romanian Ministry of Health. In a medical world where safety and quality are essential, a checklist could prevent medical errors, and team time out can ensure high-quality endoscopy, enhance teamwork and offer patients confidence in the medical team

Clinicopathological Analysis of Complicated Colorectal Cancer: A Five-Year Retrospective Study from a Single Surgery Unit

Patients with primary colorectal cancer can present with obstructions, tumor bleeding, or perforations, which represent acute complications. This paper aimed to analyze and compare the clinical and pathological profiles of two patient groups: one with colorectal cancer and a related complication and another without any specific complication. We performed a five-year retrospective study on colorectal cancer patients admitted to a surgery unit and comparatively explored the main clinical and pathological features of the tumors belonging to the two groups. A total of 250 patients with colorectal cancer were included in the analysis. Of these, 117 (46.8%) had presented a type of complication. The comparative analysis that examined several clinical and pathological parameters showed a statistically significant difference for unfavorable prognosis factors in the group with complications. This was evident for features such as vascular and perineural invasion, lymph node involvement, pathological primary tumor stage, and TNM stage. Colorectal cancers with a related complication belonged to a group of tumors with a more aggressive histopathologic profile and more advanced stages. Furthermore, the comparable incidence of cases in the two groups of patients warrants further efforts to be made in terms of early detection and prognosis prediction of colorectal cancer

Pattern Recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical learning (PARADISE): protocol for the development of an intelligent decision support system using fetal morphology ultrasound scan to detect fetal congenital anomaly detection

Introduction Congenital anomalies are the most encountered cause of fetal death, infant mortality and morbidity. 7.9 million infants are born with congenital anomalies yearly. Early detection of congenital anomalies facilitates life-saving treatments and stops the progression of disabilities. Congenital anomalies can be diagnosed prenatally through morphology scans. A correct interpretation of the morphology scan allows a detailed discussion with the parents regarding the prognosis. The central feature of this project is the development of a specialised intelligent system that uses two-dimensional ultrasound movies obtained during the standard second trimester morphology scan to identify congenital anomalies in fetuses.Methods and analysis The project focuses on three pillars: committee of deep learning and statistical learning algorithms, statistical analysis, and operational research through learning curves. The cross-sectional study is divided into a training phase where the system learns to detect congenital anomalies using fetal morphology ultrasound scan, and then it is tested on previously unseen scans. In the training phase, the intelligent system will learn to answer the following specific objectives: (a) the system will learn to guide the sonographer’s probe for better acquisition; (b) the fetal planes will be automatically detected, measured and stored and (c) unusual findings will be signalled. During the testing phase, the system will automatically perform the above tasks on previously unseen videos.Pregnant patients in their second trimester admitted for their routine scan will be consecutively included in a 32-month study (4 May 2022–31 December 2024). The number of patients is 4000, enrolled by 10 doctors/sonographers. We will develop an intelligent system that uses multiple artificial intelligence algorithms that interact between themselves, in bulk or individual. For each anatomical part, there will be an algorithm in charge of detecting it, followed by another algorithm that will detect whether anomalies are present or not. The sonographers will validate the findings at each intermediate step.Ethics and dissemination All protocols and the informed consent form comply with the Health Ministry and professional society ethics guidelines. The University of Craiova Ethics Committee has approved this study protocol as well as the Romanian Ministry of Research Innovation and Digitization that funded this research. The study will be implemented and reported in line with the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) statement.Trial registration number The study is registered under the name ‘Pattern recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical Learning’, project number 101PCE/2022, project code PN-III-P4-PCE-2021-0057. Trial registration: ClinicalTrials.gov, unique identifying number NCT05738954, date of registration: 2 November 2023

Preoperative Immunocite-Derived Ratios Predict Surgical Complications Better when Artificial Neural Networks Are Used for Analysis—A Pilot Comparative Study

We aimed to comparatively assess the prognostic preoperative value of the main peripheral blood components and their ratios—the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)—to the use of artificial-neural-network analysis in determining undesired postoperative outcomes in colorectal cancer patients. Our retrospective study included 281 patients undergoing elective radical surgery for colorectal cancer in the last seven years. The preoperative values of SII, NLR, LMR, and PLR were analyzed in relation to postoperative complications, with a special emphasis on their ability to accurately predict the occurrence of anastomotic leak. A feed-forward fully connected multilayer perceptron network (MLP) was trained and tested alongside conventional statistical tools to assess the predictive value of the abovementioned blood markers in terms of sensitivity and specificity. Statistically significant differences and moderate correlation levels were observed for SII and NLR in predicting the anastomotic leak rate and degree of postoperative complications. No correlations were found between the LMR and PLR or the abovementioned outcomes. The MLP network analysis showed superior prediction value in terms of both sensitivity (0.78 ± 0.07; 0.74 ± 0.04; 0.71 ± 0.13) and specificity (0.81 ± 0.11; 0.69 ± 0.03; 0.9 ± 0.04) for all the given tasks. Preoperative SII and NLR appear to be modest prognostic factors for anastomotic leakage and overall morbidity. Using an artificial neural network offers superior prognostic results in the preoperative risk assessment for overall morbidity and anastomotic leak rate