Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions

[EN] Deep learning-based algorithms have led to tremendous progress over the last years, but they face a bottleneck as their optimal development highly relies on access to large datasets. To mitigate this limitation, cross-silo federated learning has emerged as a way to train collaborative models among multiple institutions without having to share the raw data used for model training. However, although artificial intelligence experts have the expertise to develop state-of-the-art models and actively share their code through notebook environments, implementing a federated learning system in real-world applications entails significant engineering and deployment efforts. To reduce the complexity of federation setups and bridge the gap between federated learning and notebook users, this paper introduces a solution that leverages the Jupyter environment as part of the federated learning pipeline and simplifies its automation, the Notebook Federator. The feasibility of this approach is then demonstrated with a collaborative model solving a digital pathology image analysis task in which the federated model reaches an accuracy of 0.8633 on the test set, as compared to the centralized configurations for each institution obtaining 0.7881, 0.6514, and 0.8096, respectively. As a fast and reproducible tool, the proposed solution enables the deployment of a cross-country federated environment in only a few minutes.This work has been partially funded by the European Union s Horizon 2020 research and innovation programme with the project CLARIFY under Marie Sklodowska-Curie (860627), ENVRI-FAIR (824068), BlueCloud (862409), and ARTICONF (825134). This work is also supported by LifeWatch ERIC, GVA through projects PROMETEO/2019/109 and INNEST/2021/321 (SAMUEL), and the Spanish Ministry of Economy and Competitiveness through project PID2019-105142RB-C21 (AI4SKIN). The work of Adrián Colomer has been supported by the ValgrAI Valencian Graduate School and Research Network for Artificial Intelligence & Generalitat Valenciana and Universitat Politècnica de València (PAID-PD-22).Launet, LM.; Wang, Y.; Colomer, A.; Igual García, J.; Pulgarín-Ospina, CC.; Koulouzis, S.; Bianchi, R.... (2023). Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions. Applied Sciences. 13(2). https://doi.org/10.3390/app1302091913

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

The matricellular protein hevin is involved in alcohol use disorder

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Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions

Deep learning-based algorithms have led to tremendous progress over the last years, but they face a bottleneck as their optimal development highly relies on access to large datasets. To mitigate this limitation, cross-silo federated learning has emerged as a way to train collaborative models among multiple institutions without having to share the raw data used for model training. However, although artificial intelligence experts have the expertise to develop state-of-the-art models and actively share their code through notebook environments, implementing a federated learning system in real-world applications entails significant engineering and deployment efforts. To reduce the complexity of federation setups and bridge the gap between federated learning and notebook users, this paper introduces a solution that leverages the Jupyter environment as part of the federated learning pipeline and simplifies its automation, the Notebook Federator. The feasibility of this approach is then demonstrated with a collaborative model solving a digital pathology image analysis task in which the federated model reaches an accuracy of 0.8633 on the test set, as compared to the centralized configurations for each institution obtaining 0.7881, 0.6514, and 0.8096, respectively. As a fast and reproducible tool, the proposed solution enables the deployment of a cross-country federated environment in only a few minutes

The Matricellular Protein Hevin Is Involved in Alcohol Use Disorder

Astrocytic-secreted matricellular proteins have been shown to influence various aspects of synaptic function. More recently, they have been found altered in animal models of psychiatric disorders such as drug addiction. Hevin (also known as Sparc-like 1) is a matricellular protein highly expressed in the adult brain that has been implicated in resilience to stress, suggesting a role in motivated behaviors. To address the possible role of hevin in drug addiction, we quantified its expression in human postmortem brains and in animal models of alcohol abuse. Hevin mRNA and protein expression were analyzed in the postmortem human brain of subjects with an antemortem diagnosis of alcohol use disorder (AUD, n = 25) and controls (n = 25). All the studied brain regions (prefrontal cortex, hippocampus, caudate nucleus and cerebellum) in AUD subjects showed an increase in hevin levels either at mRNA or/and protein levels. To test if this alteration was the result of alcohol exposure or indicative of a susceptibility factor to alcohol consumption, mice were exposed to different regimens of intraperitoneal alcohol administration. Hevin protein expression was increased in the nucleus accumbens after withdrawal followed by a ethanol challenge. The role of hevin in AUD was determined using an RNA interference strategy to downregulate hevin expression in nucleus accumbens astrocytes, which led to increased ethanol consumption. Additionally, ethanol challenge after withdrawal increased hevin levels in blood plasma. Altogether, these results support a novel role for hevin in the neurobiology of AUD