Artificial Intelligence Assisted Recognition of Anatomical Landmarks and Laparoscopic Instruments in Transabdominal Preperitoneal Inguinal Hernia Repair.

Laparoscopic TAPP (Trans-Abdominal PrePeritoneal) is a minimally invasive surgical procedure used to repair inguinal hernias. Arguably, one important aspect to TAPP hernia repair is the identification of anatomical landmarks and the correct use of various laparoscopic instruments. There are very few studies regarding the use of artificial intelligence in laparoscopic inguinal hernia repair and more specifically in TAPP. The aim of this study is to evaluate the feasibility and usefulness of AI in the recognition of anatomical landmarks and tools in laparoscopic TAPP videos. Imaging data have been exported from 20 Laparoscopic TAPP videos that have been performed by the authors and another 5 high quality TAPP videos from the internet (free access) performed by other surgeons. In total 1095 selected images have been exported for annotation. To accomplish the AI result of computer vision, the YOLOv8 model of deep learning was used. In total 2716 segmented areas of interest have been exported. The AI model was able to detect the various classes with a maximum F1 score of .82 when the confidence threshold was set to .406. The mAP50 was .873 for all classes. The Precision was above 50% when the confidence was over 10%. The Recall rate was above 50% when confidence was less than 70%. These results suggest that the model is effective at balancing precision and recall, capturing both true positives and minimizing false negatives. Artificial Intelligence recognition of anatomical landmarks and laparoscopic instruments in TAPP is feasible with acceptable success rates.

Automated ML Techniques for Predicting COVID-19 Mortality in the ICU.

The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission to the intensive care unit, as well as to predict the demand for hospital beds, equipment, and staff in the future. We retrospectively analyzed demographics, and routine blood biomarkers from consecutive Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, during a 17-month period, relative to the outcome, in order to build a prognostic model. We used the Google Vertex AI platform, on the one hand, to evaluate its performance in predicting ICU mortality, and on the other hand to show the ease with which even non-experts can make prognostic models. The model's performance regarding the area under the receiver operating characteristic curve (AUC-ROC) was 0.955. The six highest-ranked predictors of mortality in the prognostic model were age, serum urea, platelets, C-reactive protein, hemoglobin, and SGOT.

Prediction of COVID-19 Mortality in the Intensive Care Unit Using Machine Learning.

Since its emergence, the COVID-19 pandemic still poses a major global health threat. In this setting, a number of useful machine learning applications have been explored to assist clinical decision-making, predict the severity of disease and admission to the intensive care unit, and also to estimate future demand for hospital beds, equipment, and staff. The present study examined demographic data, hematological and biochemical markers routinely measured in Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, in relation to the ICU outcome, during the second and third Covid-19 waves, from October 2020 until February 2022. In this dataset, we applied eight well-known classifiers of the caret package for machine learning of the R programming language, to evaluate their performance in forecasting ICU mortality. The best performance regarding area under the receiver operating characteristic curve (AUC-ROC) was observed with Random Forest (0.82), while k-nearest neighbors (k-NN) were the lowest performing machine learning algorithm (AUC-ROC: 0.59). However, in terms of sensitivity, XGB outperformed the other classifiers (max Sens: 0.7). The six most important predictors of mortality in the Random Forest model were serum urea, age, hemoglobin, C-reactive protein, platelets, and lymphocyte count.

Analyzing Sequence Data with Markov Chain Models in Scientific Experiments.

Virtual reality-based instruction is becoming an important resource to improve learning outcomes and communicate hands-on skills in science laboratory courses. Our study attempts first to investigate whether a Markov chain model can predict the students' performance in conducting an experiment and whether simulations improve learner achievement in handling lab equipment and conducting science experiments in physical labs. In the present study, three cohorts of graduate students are trained on a microscopy experiment using different teaching methodologies. The effectiveness of the teaching strategies is evaluated by observing the sequences of students' actions, while engaging in the microscopy experiment in real-lab situations. The students' ability in performing the science experiment is estimated by sequential analysis using a Markov chain model. According to the Markov chain analysis, the students who are trained via a virtual reality software exhibit a higher probability to perform the steps of the experiment without difficulty and without assistance than their fellow students who attend more traditional training scenarios. Our study indicates that a Markov chain model is a powerful tool that can lead to a dynamic evaluation of the students' performance in science experiments by tracing the students' knowledge states and by predicting their innate abilities.

Implementation and Evaluation of a Three-Dimensional Virtual Reality Biology Lab versus Conventional Didactic Practices in Lab Experimenting with the Photonic Microscope.

This study presents the integration of three different teaching scenarios, during biology laboratory lessons, with the overall aim of exploring the potential predominant effectiveness of teaching and improvement of students' learning, by the use of the three-dimensional virtual reality educational tool Onlabs, versus more traditional didactic practices. A sample of 83, fourth year, undergraduate students of the Primary Education Department of Patras' University in Greece, were equally separated into three cognitively balanced groups to be educated on the light microscopy experiment by three different educational scenarios. Students' conceptual understanding in the domain of microscopy, was evaluated during all learning procedure with Pre and Post tests, whereas their skill to handle properly a real light microscope in the wet biology lab was summatively assessed via a specially designed work sheet. Results of the present study provide evidence in favor of the virtual reality application. © 2019 International Union of Biochemistry and Molecular Biology, 48(1):21-27, 2020.