Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome.

The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.

Artificial intelligence in diagnostic and interventional radiology: Where are we now?

The emergence of massively parallel yet affordable computing devices has been a game changer for research in the field of artificial intelligence (AI). In addition, dramatic investment from the web giants has fostered the development of a high-quality software stack. Going forward, the combination of faster computers with dedicated software libraries and the widespread availability of data has opened the door to more flexibility in the design of AI models. Radiomics is a process used to discover new imaging biomarkers that has multiple applications in radiology and can be used in conjunction with AI. AI can be used throughout the various processes of diagnostic imaging, including data acquisition, reconstruction, analysis and reporting. Today, the concept of "AI-augmented" radiologists is preferred to the theory of the replacement of radiologists by AI in many indications. Current evidence bolsters the assumption that AI-assisted radiologists work better and faster. Interventional radiology becomes a data-rich specialty where the entire procedure is fully recorded in a standardized DICOM format and accessible via standard picture archiving and communication systems. No other interventional specialty can bolster such readiness. In this setting, interventional radiology could lead the development of AI-powered applications in the broader interventional community. This article provides an update on the current status of radiomics and AI research, analyzes upcoming challenges and also discusses the main applications in AI in interventional radiology to help radiologists better understand and criticize articles reporting AI in medical imaging.

Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications.

Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.

Accelerating High-Dimensional Temporal Modelling Using Graphics Processing Units for Pharmacovigilance Signal Detection on Real-Life Data.

Adverse drug reaction is a major public health issue. The increasing availability of medico-administrative databases offers major opportunities to detect real-life pharmacovigilance signals. We have recently adapted a pharmacoepidemiological method to the large dimension, the WCE (Weigthed Cumulative Exposure) statistical model, which makes it possible to model the temporal relationship between the prescription of a drug and the appearance of a side effect without any a priori hypothesis. Unfortunately, this method faces a computational time problem. The objective of this paper is to describe the implementation of the WCE statistical model using Graphics Processing Unit (GPU) programming as a tool to obtain the spectrum of adverse drug reactions from medico-administrative databases. The process is divided into three steps: pre-processing of care pathways using the Python library Panda, calculation of temporal co-variables using the Python library "KeOps", estimation of the model parameters using the Python library "PyTorch" - standard in deep learning. Programming the WCE method by distributing the heaviest portions (notably spline calculation) on the GPU makes it possible to accelerate the time required for this method by 1000 times using a computer graphics card and up to 10,000 times with a GPU server. This implementation makes it possible to use WCE on all the drugs on the market to study their spectrum of adverse effects, to highlight new vigilance signals and thus to have a global vigilance tool on medico-administrative database. This is a proof of concept for the use of this technology in epidemiology.