Image-Guided Surgical e-Learning in the Post-COVID-19 Pandemic Era: What Is Next?

The current unprecedented coronavirus 2019 (COVID-19) crisis has accelerated and enhanced e-learning solutions. During the so-called transition phase, efforts were made to reorganize surgical services, reschedule elective surgical procedures, surgical research, academic education, and careers to optimize results. The intention to switch to e-learning medical education is not a new concern. However, the current crisis triggered an alarm to accelerate the transition. Efforts to consider e-learning as a teaching and training method for medical education have proven to be efficient. For image-guided therapies, the challenge requires more effort since surgical skills training is combined with image interpretation training, thus the challenge is to cover quality educational content with a balanced combination of blended courses (online/onsite). Several e-resources are currently available in the surgical scenario; however, further efforts to enhance the current system are required by accelerating the creation of new learning solutions to optimize complex surgical education needs in the current disrupted environment

Analysis of cavitation artifacts in Magnetic Resonance Imaging Thermometry during laser ablation monitoring

: Magnetic Resonance Thermometry Imaging (MRTI) holds great potential in laser ablation (LA) monitoring. It provides the real-time multidimensional visualization of the treatment effect inside the body, thus enabling accurate intraoperative prediction of the thermal damage induced. Despite its great potential., thermal maps obtained with MRTI may be affected by numerous artifacts. Among the sources of error producing artifacts in the images., the cavitation phenomena which could occur in the tissue during LA induces dipole-structured artifacts. In this work., an analysis of the cavitation artifacts occurring during LA in a gelatin phantom in terms of symmetry in space and symmetry of temperature values was performed. Results of 2 Wand 4 W laser power were compared finding higher symmetry for the 2 W case in terms of both dimensions of artifact-lobes and difference in temperature values extracted in specular pixels in the image. This preliminary investigation of artifact features may provide a step forward in the identification of the best strategy to correct and avoid artifact occurrence during thermal therapy monitoring. Clinical Relevance- This work presents an analysis of cavitation artifacts in MRTI from LA which must be corrected to avoid error in the prediction of thermal damage during LA monitoring

Radiomic analysis of abdominal organs during sepsis of digestive origin in a French intensive care unit

Background Sepsis is a severe and common cause of admission to the intensive care unit (ICU). Radiomic analysis (RA) may predict organ failure and patient outcomes. The objective of this study was to assess a model of RA and to evaluate its performance in predicting in-ICU mortality and acute kidney injury (AKI) during abdominal sepsis. Methods This single-center, retrospective study included patients admitted to the ICU for abdominal sepsis. To predict in-ICU mortality or AKI, elastic net regularized logistic regression and the random forest algorithm were used in a five-fold cross-validation set repeated 10 times. Results Fifty-five patients were included. In-ICU mortality was 25.5%, and 76.4% of patients developed AKI. To predict in-ICU mortality, elastic net and random forest models, respectively, achieved areas under the curve (AUCs) of 0.48 (95% confidence interval [CI], 0.43–0.54) and 0.51 (95% CI, 0.46–0.57) and were not improved combined with Simplified Acute Physiology Score (SAPS) II. To predict AKI with RA, the AUC was 0.71 (95% CI, 0.66–0.77) for elastic net and 0.69 (95% CI, 0.64–0.74) for random forest, and these were improved combined with SAPS II, respectively; AUC of 0.94 (95% CI, 0.91–0.96) and 0.75 (95% CI, 0.70–0.80) for elastic net and random forest, respectively. Conclusions This study suggests that RA has poor predictive performance for in-ICU mortality but good predictive performance for AKI in patients with abdominal sepsis. A secondary validation cohort is needed to confirm these results and the assessed model

Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective

International audienceResearch in medical imaging has yet to do to achieve precision oncology. Over the past 30 years, only the simplest imaging biomarkers (RECIST, SUV,…) have become widespread clinical tools. This may be due to our inability to accurately characterize tumors and monitor intratumoral changes in imaging. Artificial intelligence, through machine learning and deep learning, opens a new path in medical research because it can bring together a large amount of heterogeneous data into the same analysis to reach a single outcome. Supervised or unsupervised learning may lead to new paradigms by identifying unrevealed structural patterns across data. Deep learning will provide human-free, undefined upstream, reproducible, and automated quantitative imaging biomarkers. Since tumor phenotype is driven by its genotype and thus indirectly defines tumoral progression, tumor characterization using machine learning and deep learning algorithms will allow us to monitor molecular expression noninvasively, anticipate therapeutic failure, and lead therapeutic management. To follow this path, quality standards have to be set: standardization of imaging acquisition as it has been done in the field of biology, transparency of the model development as it should be reproducible by different institutions, validation, and testing through a high-quality process using large and complex open databases and better interpretability of these algorithms

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks

Abstract The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting the number of positive and hospitalized cases via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. By leveraging population mobility data and other features, the model utilizes message passing to model interaction between areas. Our proposed model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning

How Using Dedicated Software Can Improve RECIST Readings

Decision support tools exist for oncologic follow up. Their main interest is to help physicians improve their oncologic readings but this theoretical benefit has to be quantified by concrete evidence. The purpose of the study was to evaluate and quantify the impact of using dedicated software on RECIST readings. A comparison was made between RECIST readings without dedicated application vs. readings using dedicated software (Myrian® XL-Onco, Intrasense, France) with specific functionalities such as 3D elastic target matching and automated calculation of tumoral response. A retrospective database of 40 patients who underwent a CT scan follow up was used (thoracic/abdominal lesions). The reading panel was composed of two radiologists. Reading times, intra/inter-operator reproducibility of measurements and RECIST response misclassifications were evaluated. On average, reading time was reduced by 49.7% using dedicated software. A more important saving was observed for lung lesions evaluations (63.4% vs. 36.1% for hepatic targets). Inter and intra-operator reproducibility of measurements was excellent for both reading methods. Using dedicated software prevented misclassifications on 10 readings out of 120 (eight due to calculation errors). The use of dedicated oncology software optimises RECIST evaluation by decreasing reading times significantly and avoiding response misclassifications due to manual calculation errors or approximations

Demystification of AI-driven medical image interpretation: past, present and future

International audienceThe recent explosion of ‘big data’ has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it

Magnetic Resonance Enterography in the Study of Patients With Crohn's Disease: Which Findings Are More Likely to Change Patient Management?

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Can magnetic resonance spectroscopy differentiate malignant and benign causes of lymphadenopathy? An in-vitro approach

Lymphadenopathy continues to be a common problem to radiologists and treating physiciansbecause of the difficulty in confidently categorizing a node as being benign or malignantusing standard diagnostic techniques. The goal of our research was to assess whethermagnetic resonance (MR) spectroscopy contains the necessary information to allow differentiationof benign from malignant lymph nodes in an in-vitro approach using a modern patternrecognition method. Tissue samples from a tissue bank were analyzed on a nuclearmagnetic resonance (NMR) spectrometer. A total of 69 samples were studied. The samplesincluded a wide variety of malignant and benign etiologies. Using 45 samples, we initiallycreated a model which was able to predict if a certain spectrum originates from benign ormalignant lymph nodes using a pattern-recognition technique which takes into account theentire magnetic spectrum rather than single peaks alone. The remaining 24 samples wereblindly loaded in the model to assess its performance. We obtained an excellent accuracy indifferentiating benign and malignant lymphadenopathy using the model. It correctly differentiatedas malignant or benign, in a blinded fashion, all of the malignant samples (13 of 13)and 10 out of the 11 benign samples. We thus showed that magnetic spectroscopy is able todifferentiate benign from malignant causes of lymphadenopathy. [...