J Ultrasound Med

Chest CT is the reference test for assessing pulmonary injury in suspected or diagnosed COVID-19 with signs of clinical severity. This study aimed to evaluate the association of a lung ultrasonography score and unfavorable clinical evolution at 28 days. The eChoVid is a multicentric study based on routinely collected data that was conducted in 8 emergency units in France; patients were included between March 19, 2020 and April 28, 2020 and underwent lung ultrasonography, a short clinical assessment by 2 emergency physicians blinded to each other's assessment, and chest CT. Lung ultrasonography consisted of scoring lesions from 0 to 3 in 8 chest zones, thus defining a global score (GS) of severity from 0 to 24. The primary outcome was the association of lung damage severity as assessed by the GS at day 0 and patient status at 28 days. Secondary outcomes were comparing the performance between GS and CT scan and the performance between a new trainee physician and an ultrasonography expert in scores. For the 328 patients analyzed, the GS showed good performance in predicting clinical worsening at 28 days (area under the receiver operating characteristic curve [AUC] 0.83, sensitivity 84.2%, specificity 76.4%). The GS showed good performance in predicting the CT severity assessment (AUC 0.84, sensitivity 77.2%, specificity 83.7%). A lung ultrasonography GS is a simple tool that can be used in the emergency department to predict unfavorable assessment at 28 days in patients with COVID-19

Microlocal sheaf theory for Radon transform

Le sujet de cette thèse est une approche microlocale de la transformation de Radon. Il s’agit d’appliquer à la dualité projective complexe et réelle les techniques initiées dans l’article fondateur de Sato-Kashiwara-Kawai de 1972 et de retrouver, reformuler, améliorer des travaux d’analyse plus classiques sur ce sujet, en particulier ceux de G. Henkin ou S. Gindikin. La dualité projective vue sous l’angle microlocal et faisceautique est apparue pour la première fois dans un travail important de J-L. Brylinski sur les faisceaux pervers, travail repris ensuite par D’Agnolo et Schapira dans le cadre des D-modules. Notre travail est de reprendre systématiquement cette étude avec les nouveaux outils de la théorie microlocale des faisceaux (théorie qui n’existait pas à l’époque de SKK72). Ce travail se compose essentiellement de deux parties. Dans la première, nous commençons par rappeler dans un cadre général la construction des transformations canoniques quantifiées, sous l’hypothèse de l’existence d’une section simple non-dégénérée (introduite sous un autre nom par J. Leray). Cette construction n’avait jamais été faite dans un cadre global hors du cas projectif. Nous montrons alors que ces transformations commutent à l’action des opérateurs microdifferentiels. Il s’agit là d’un résultat fondamental sans qu’aucune preuve consistante n’existe dans la littérature, ce résultat étant plus ou moins sous-entendu dans SKK72. La deuxième partie de la thèse traite des applications à la transformation de Radon “clas-sique”. L’idée de base est que cette transformation échange support des hyperfonctions (modulo analyticité) et front d’onde analytique. Nous obtenons ainsi des théorèmes de prolongement ou d’unicité sur les ouverts linéellement concave. Nous obtenons aussi un théorème des résidus pour les valeurs au bord de classes de cohomologie définies sur les cônes de signatures (1, n − 1), clarifiant substantiellement des travaux de Cordaro-Gindikin-Trèves.The subject of this thesis is a microlocal approach to the transformation of Radon. It is a question of applying to real and complex projective duality the techniques initiated in the founding article of Sato-Kashiwara-Kawai of 1972 and to find, reformulate, improve more classic analytical work on this subject, in particular those of G. Henkin or S. Gindikin. Pro-jective duality seen from the microlocal and sheaf point of view appeared for the first time in an important work by J-L. Brylinski on perverse sheaves, work then taken up by D'Agnolo and Schapira in the framework of D-modules. Our work is to systematically resume this study with the new tools of the microlocal sheaf theory (theory which did not exist at the time of SKK72). This work essentially consists of two parts. In the first, we begin by recalling in a general framework the construction of quantized ca-nonical transformations, under the hypothesis of the existence of a simple non-degenerate section (introduced under another name by J. Leray). This construction had never been done in a global framework outside the projective case. We then show that these transfor-mations exchange the action of the microdifferential operators. This is a fundamental re-sult without any consistent proof existing in the literature, this result being more or less implied in SKK72. The second part of the thesis deals with the applications to the “classical” Radon trans-form. The basic idea is that this transform exchanges the support of hyperfunctions (modu-lo analyticity) and the analytic wavefront set. We thus obtain theorems of continuation or uniqueness on linearly concave domain. We also get a residue theorem for the boundary values of finite cohomology classes defined on cones with (1, n-1) signature, substantially clari-fying the work of Cordaro-Gindikin-Trèves

Blockchain technology for improving clinical research quality

Abstract Reproducibility, data sharing, personal data privacy concerns and patient enrolment in clinical trials are huge medical challenges for contemporary clinical research. A new technology, Blockchain, may be a key to addressing these challenges and should draw the attention of the whole clinical research community. Blockchain brings the Internet to its definitive decentralisation goal. The core principle of Blockchain is that any service relying on trusted third parties can be built in a transparent, decentralised, secure “trustless” manner at the top of the Blockchain (in fact, there is trust, but it is hardcoded in the Blockchain protocol via a complex cryptographic algorithm). Therefore, users have a high degree of control over and autonomy and trust of the data and its integrity. Blockchain allows for reaching a substantial level of historicity and inviolability of data for the whole document flow in a clinical trial. Hence, it ensures traceability, prevents a posteriori reconstruction and allows for securely automating the clinical trial through what are called Smart Contracts. At the same time, the technology ensures fine-grained control of the data, its security and its shareable parameters, for a single patient or group of patients or clinical trial stakeholders. In this commentary article, we explore the core functionalities of Blockchain applied to clinical trials and we illustrate concretely its general principle in the context of consent to a trial protocol. Trying to figure out the potential impact of Blockchain implementations in the setting of clinical trials will shed new light on how modern clinical trial methods could evolve and benefit from Blockchain technologies in order to tackle the aforementioned challenges

Blockchain protocols in clinical trials: Transparency and traceability of consent [version 4; referees: 1 approved, 2 approved with reservations, 2 not approved]

Clinical trial consent for protocols and their revisions should be transparent for patients and traceable for stakeholders. Our goal is to implement a process allowing for collection of patients’ informed consent, which is bound to protocol revisions, storing and tracking the consent in a secure, unfalsifiable and publicly verifiable way, and enabling the sharing of this information in real time. For that, we build a consent workflow using a trending technology called Blockchain. This is a distributed technology that brings a built-in layer of transparency and traceability. From a more general and prospective point of view, we believe Blockchain technology brings a paradigmatical shift to the entire clinical research field. We designed a Proof-of-Concept protocol consisting of time-stamping each step of the patient’s consent collection using Blockchain, thus archiving and historicising the consent through cryptographic validation in a securely unfalsifiable and transparent way. For each protocol revision, consent was sought again.  We obtained a single document, in an open format, that accounted for the whole consent collection process: a time-stamped consent status regarding each version of the protocol. This document cannot be corrupted and can be checked on any dedicated public website. It should be considered a robust proof of data. However, in a live clinical trial, the authentication system should be strengthened to remove the need for third parties, here trial stakeholders, and give participative control to the peer users. In the future, the complex data flow of a clinical trial could be tracked by using Blockchain, which core functionality, named Smart Contract, could help prevent clinical trial events not occurring in the correct chronological order, for example including patients before they consented or analysing case report form data before freezing the database. Globally, Blockchain could help with reliability, security, transparency and could be a consistent step toward reproducibility

Blockchain protocols in clinical trials: Transparency and traceability of consent [version 3; referees: 1 approved, 2 approved with reservations, 1 not approved]

Clinical trial consent for protocols and their revisions should be transparent for patients and traceable for stakeholders. Our goal is to implement a process allowing the collection of patients’ informed consent, which is bound to protocol revisions, storing and tracking the consent in a secure, unfalsifiable and publicly verifiable way, and enabling the sharing of this information in real time. For that, we will built a consent workflow using a rising technology called Blockchain. This is a distributed technology that brings a built-in layer of transparency and traceability. From a more general and prospective point of view, we believe Blockchain technology brings a paradigmatical shift to the entire clinical research field. We designed a Proof-of-Concept protocol consisting of time-stamping each step of the patient’s consent collection using Blockchain; thus archiving and historicising the consent through cryptographic validation in a securely unfalsifiable and transparent way. For each revision of the protocol, consent was sought again. We obtained a single document, in a standard open format, that accounted for the whole consent collection process: timestamped consent status with regards to each version of the protocol. This document cannot be corrupted, and can be checked on any dedicated public website. It should be considered as a robust proof of data. However, in a live clinical trial, the authentication system should be strengthened in order to remove the need for third parties, here the trial stakeholders, and give participative control to the peer-to-peer users. In the future, we think that the complex data flow of a clinical trial can be tracked using Blockchain, that a blockchain core functionality, named Smart Contract, could help prevent clinical trial events not to happen in the right chronological order: for example including patients before they consented or analysing case report forms data before freezing the database. Globally, we think Blockchain will help with reliability, security, and transparency, and could be a consistent step towards reproducibility

echOpen-Cardio: Low-cost Open POCUS & Robust AI for Cardiac Image Analysis

International audienceIn this paper, we are going to present the echOpen-Cardio project, which aims at developing low-cost open-source point-of-care ultrasound devices combined with robust artificial intelligence (AI) tools for cardiac disease diagnosis and therapy. We will first review the current market of ultrasound and the role of point of care ultrasound (POCUS), then present the echOpen project and its achievements by now. Finally, we will present an example of robust AI tools for cardiac ultrasound segmentation that leverage shape priors. This has great potential to be applied for the echOpen-Cardio platform

Fold-stratified cross-validation for unbiased and privacy-preserving federated learning

International audienceAbstract Objective We introduce fold-stratified cross-validation, a validation methodology that is compatible with privacy-preserving federated learning and that prevents data leakage caused by duplicates of electronic health records (EHRs). Materials and Methods Fold-stratified cross-validation complements cross-validation with an initial stratification of EHRs in folds containing patients with similar characteristics, thus ensuring that duplicates of a record are jointly present either in training or in validation folds. Monte Carlo simulations are performed to investigate the properties of fold-stratified cross-validation in the case of a model data analysis using both synthetic data and MIMIC-III (Medical Information Mart for Intensive Care-III) medical records. Results In situations in which duplicated EHRs could induce overoptimistic estimations of accuracy, applying fold-stratified cross-validation prevented this bias, while not requiring full deduplication. However, a pessimistic bias might appear if the covariate used for the stratification was strongly associated with the outcome. Discussion Although fold-stratified cross-validation presents low computational overhead, to be efficient it requires the preliminary identification of a covariate that is both shared by duplicated records and weakly associated with the outcome. When available, the hash of a personal identifier or a patient’s date of birth provides such a covariate. On the contrary, pseudonymization interferes with fold-stratified cross-validation, as it may break the equality of the stratifying covariate among duplicates. Conclusion Fold-stratified cross-validation is an easy-to-implement methodology that prevents data leakage when a model is trained on distributed EHRs that contain duplicates, while preserving privacy