Comment lire un article traitant de l’intelligence artificielle (IA)

Source : BMJ Medicine

Auteurs : Paul Dijkstra, Trisha Greenhalgh, Yosra Magdi Mekki, Jessica Morley

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Abstract : This paper guides readers through the critical appraisal of a paper that includes the use of artificial intelligence (AI) in clinical settings for healthcare delivery. A brief introduction to the different types of AI used in healthcare is given, along with some ethical principles to guide the introduction of AI systems into healthcare. Existing publication guidelines for AI studies are highlighted. Ten preliminary questions to ask about a paper describing an AI based decision support algorithm are suggested.
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FaceAge, un système d’apprentissage profond permettant d’estimer l’âge biologique à partir de photographies du visage afin d’améliorer le pronostic : étude de développement et de validation d’un modèle

Source : The Lancet Digital Health

Auteurs : Dennis Bontempi, Osbert Zalay, Danielle S Bitterman, Nicolai Birkbak, Derek Shyr, Fridolin Haugg, Jack M Qian, Hannah Roberts, Subha Perni, Vasco Prudente, Suraj Pai, Andre Dekker, Benjamin Haibe-Kains, Christian Guthier, Tracy Balboni, Laura Warren, Monica Krishan, Benjamin H Kann, Charles Swanton, Dirk De Ruysscher, Raymond H Mak, Hugo J W L Aerts.

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Abstract :

Background: As humans age at different rates, physical appearance can yield insights into biological age and physiological health more reliably than chronological age. In medicine, however, appearance is incorporated into medical judgements in a subjective and non-standardised way. In this study, we aimed to develop and validate FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs.
Methods: FaceAge was trained on data from 58 851 presumed healthy individuals aged 60 years or older: 56 304 individuals from the IMDb-Wiki dataset (training) and 2547 from the UTKFace dataset (initial validation). Clinical utility was evaluated on data from 6196 patients with cancer diagnoses from two institutions in the Netherlands and the USA: the MAASTRO, Harvard Thoracic, and Harvard Palliative cohorts FaceAge estimates in these cancer cohorts were compared with a non-cancerous reference cohort of 535 individuals. To assess the prognostic relevance of FaceAge, we performed Kaplan-Meier survival analysis and Cox modelling, adjusting for several clinical covariates. We also assessed the performance of FaceAge in patients with metastatic cancer receiving palliative treatment at the end of life by incorporating FaceAge into clinical prediction models. To evaluate whether FaceAge has the potential to be a biomarker for molecular ageing, we performed a gene-based analysis to assess its association with senescence genes.
Findings: FaceAge showed significant independent prognostic performance in various cancer types and stages. Looking older was correlated with worse overall survival (after adjusting for covariates per-decade hazard ratio [HR] 1·151, p=0·013 in a pan-cancer cohort of n=4906; 1·148, p=0·011 in a thoracic cohort of n=573; and 1·117, p=0·021 in a palliative cohort of n=717). We found that, on average, patients with cancer looked older than their chronological age (mean increase of 4·79 years with respect to non-cancerous reference cohort, p<0·0001). We found that FaceAge can improve physicians’ survival predictions in patients with incurable cancer receiving palliative treatments (from area under the curve 0·74 [95% CI 0·70-0·78] to 0·8 [0·76-0·83]; p<0·0001), highlighting the clinical use of the algorithm to support end-of-life decision making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, whereas age was not.
Interpretation: Our results suggest that a deep learning model can estimate biological age from face photographs and thereby enhance survival prediction in patients with cancer. Further research, including validation in larger cohorts, is needed to verify these findings in patients with cancer and to establish whether the findings extend to patients with other diseases. Subject to further testing and validation, approaches such as FaceAge could be used to translate a patient’s visual appearance into objective, quantitative, and clinically valuable measures.
Funding: US National Institutes of Health and EU European Research Council.

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Prédiction, à l’aide de l’apprentissage automatique, du risque de décès et d’hospitalisation chez les patients porteurs d’un défibrillateur automatique implantable

Source : JACC

Auteurs : Lindsey Rosman, Rachel Lampert, Kaicheng Wang, Anil K Gehi, James Dziura, Elena Salmoirago-Blotcher, Cynthia Brandt, Samuel F Sears, Matthew Burg

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Abstract :

Background: Predicting the clinical trajectory of individual patients with implantable cardioverter-defibrillators (ICDs) is essential to inform clinical care. Machine learning approaches can potentially overcome the limitations of conventional statistical methods and provide more accurate, personalized risk estimates.
Objectives: The authors sought to develop and externally validate a novel machine learning algorithm for predicting all-cause mortality and/or heart failure (HF) hospitalization in ICD patients with and without cardiac resynchronization therapy (CRT) using variables that are readily available to treating clinicians. We also sought to identify key factors that separate patients along a continuum of risk.
Methods: Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict 3-month and 1-year risks for all-cause mortality and a composite outcome of death/HF hospitalization during the first 5 years of device implant. Models were trained using a nationwide cohort from the Veterans Health Administration. Three models were sequentially tested, and external validation was performed in a separate nonveteran clinical registry.
Results: The training and validation cohorts included 12,043 patients (age 67.5 ± 9.4 years) and 1,394 patients (age 66.3 ± 11.9 years), respectively. Median follow-up was 3.3 years for the training cohort and 3.6 years for validation cohort. The most accurate models for both outcomes included baseline demographics entered at the time of ICD implant (age, sex, CRT therapy) and time-varying ICD data with area under the receiver-operating characteristic curve for predicting death at 3 months (0.91; 95% CI: 0.87-0.94) and 1 year (0.80; 95% CI: 0.78-0.82); death/HF hospitalization at 3 months (0.81; 95% CI: 0.79-0.83) and 1 year (0.71; 95% CI: 0.70-0.72). Models demonstrated high discrimination and good calibration in the validation cohort. Additionally, time-varying physiologic data from ICDs, especially daily physical activity, had substantial importance in predicting outcomes.
Conclusions: The RF-SLAM algorithm accurately predicted all-cause mortality and death/HF hospitalization at 3 months and 1 year during the first 5 years of device implant, demonstrating good internal and external validity. Prospective studies and randomized trials are needed to evaluate model performance in other populations and settings and to determine its impact on patient outcomes.

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Essai randomisé portant sur un chatbot basé sur l’IA générative destiné au traitement des troubles mentaux

Source : NEJM AI

Auteurs : Michael V. Heinz, M.D., Daniel M. Mackin, Ph.D., Brianna M. Trudeau, B.A., Sukanya Bhattacharya, B.A., Yinzhou Wang, M.S., Haley A. Banta, Abi D. Jewett, B.A., Abigail J. Salzhauer, B.A., Tess Z. Griffin, Ph.D. and Nicholas C. Jacobson, Ph.D.

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Analyse, optimisée par l’intelligence artificielle, des caractéristiques radiomiques issues de l’échocardiographie pour la détection de l’hypertrophie myocardique et la différenciation de son étiologie

Source : Circ Cardiovasc Imaging

Auteurs : Inki Moon, Jina Lee, Seung-Ah Lee, Dawun Jeong, Jaeik Jeon, Yeonggul Jang, Sihyeon Jeong, Jiyeon Kim, Hong-Mi Choi, In-Chang Hwang, Youngtaek Hong, Goo-Yeong Cho, Yeonyee E Yoon, Hyuk-Jae Chang

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Abstract :

Background: While echocardiography is pivotal for detecting left ventricular hypertrophy (LVH), it struggles with etiology differentiation. To enhance LVH assessment, we aimed to develop an artificial intelligence algorithm using echocardiography-based radiomics. This algorithm is designed to detect LVH and differentiate its common etiologies, such as hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HHD), based on echocardiographic images.
Methods: The developmental data sets from multiple medical centers included 867 subjects, with an independent external test set from a single tertiary medical center containing 619 subjects. Radiomic feature analysis was conducted on 4 echocardiographic views, extracting both conventional and harmonization-driven myocardial textures along with myocardial geographic features. Then, we developed classification models for each condition. Variable contributions were evaluated using Shapley Additive Explanations analysis.
Results: The radiomics-based LightGBM model, selected from internal validation, maintained strong performance in the external test set (area under the curve of 0.96 for HCM, 0.89 for CA, and 0.86 for HHD). Compared with the logistic regression model using conventional echocardiographic parameters (left ventricular ejection fraction, left ventricular mass index, left atrial volume index, and E/e’), the final model demonstrated superior sensitivity (0.89 versus 0.80 for HCM, 0.80 versus 0.80 for CA, and 0.75 versus 0.33 for HHD) and F1-score (0.87 versus 0.57 for HCM, 0.84 versus 0.72 for CA, and 0.82 versus 0.50 for HHD). Feature analysis highlighted that harmonization-driven textures played a key role in differentiating HCM, while conventional textures and myocardial thickness were influential in differentiating CA and HHD.
Conclusions: This study confirms that artificial intelligence-enhanced echocardiography-based radiomics effectively differentiate the etiology of LVH, highlighting the potential of artificial intelligence-driven texture and geographic analysis in LVH evaluation.

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Simulateur de cerveau de type « jumeau numérique » pour la surveillance en temps réel de la conscience et l’intervention virtuelle à partir de données d’électrocorticogrammes de primates

Source : NPJ Digit Med

Auteurs :Yuta Takahashi, Hayato Idei, Misako Komatsu, Jun Tani, Hiroaki Tomita, Yuichi Yamashita

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Abstract :

At the forefront of bridging computational brain modeling with personalized medicine, this study introduces a novel, real-time, electrocorticogram (ECoG) simulator, based on the digital twin brain concept. Utilizing advanced data assimilation techniques, specifically a Variational Bayesian Recurrent Neural Network model with hierarchical latent units, the simulator dynamically predicts ECoG signals reflecting real-time brain latent states. By assimilating broad ECoG signals from macaque monkeys across awake and anesthetized conditions, the model successfully updated its latent states in real-time, enhancing precision of ECoG signal simulations. Behind successful data assimilation, self-organization of latent states in the model was observed, reflecting brain states and individuality. This self-organization facilitated simulation of virtual drug administration and uncovered functional networks underlying changes in brain function during anesthesia. These results show that the proposed model can simulate brain signals in real-time with high accuracy and is also useful for revealing underlying information processing dynamics.

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L’application de l’intelligence artificielle à l’échographie dans le diagnostic des pathologies de l’endomètre : pratique actuelle et perspectives d’avenir

Source : Digit Health

Auteurs : Qiao Wei, Zhang Xiao, Xiaowen Liang, Zhili Guo, Yanfen Zhang, Zhiyi Chen

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Abstract :

Diagnosis and treatment of endometrial diseases are crucial for women’s health. Over the past decade, ultrasound has emerged as a non-invasive, safe, and cost-effective imaging tool, significantly contributing to endometrial disease diagnosis and generating extensive datasets. The introduction of artificial intelligence has enabled the application of machine learning and deep learning to extract valuable information from these datasets, enhancing ultrasound diagnostic capabilities. This paper reviews the progress of artificial intelligence in ultrasound image analysis for endometrial diseases, focusing on applications in diagnosis, decision support, and prognosis analysis. We also summarize current research challenges and propose potential solutions and future directions to advance ultrasound artificial intelligence technology in endometrial disease diagnosis, ultimately improving women’s health through digital tools.

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