The Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection During Clinical Trials

In medical research, the traditional way to collect data, i.e. browsing patient files, has been proven to induce bias, errors, human labor and costs. We propose a semi-automated system able to extract every type of data, including notes. The Smart Data Extractor pre-populates clinic research forms by following rules. We performed a cross-testing experiment to compare semi-automated to manual data collection. 20 target items had to be collected for 79 patients. The average time to complete one form was 6'81'' for manual data collection and 3'22'' with the Smart Data Extractor. There were also more mistakes during manual data collection (163 for the whole cohort) than with the Smart Data Extractor (46 for the whole cohort). We present an easy to use, understandable and agile solution to fill out clinical research forms. It reduces human effort and provides higher quality data, avoiding data re-entry and fatigue induced errors.Comment: IOS Press, 2023, Studies in Health Technology and Informatic

AI-based diagnosis and phenotype – Genotype correlations in syndromic craniosynostoses

Apert (AS), Crouzon (CS), Muenke (MS), Pfeiffer (PS), and Saethre Chotzen (SCS) are among the most frequently diagnosed syndromic craniosynostoses. The aims of this study were (1) to train an innovative model using artificial intelligence (AI)–based methods on two-dimensional facial frontal, lateral, and external ear photographs to assist diagnosis for syndromic craniosynostoses vs controls, and (2) to screen for genotype/phenotype correlations in AS, CS, and PS. We included retrospectively and prospectively, from 1979 to 2023, all frontal and lateral pictures of patients genetically diagnosed with AS, CS, MS, PS and SCS syndromes. After a deep learning–based preprocessing, we extracted geometric and textural features and used XGboost (eXtreme Gradient Boosting) to classify patients. The model was tested on an independent international validation set of genetically confirmed patients and non-syndromic controls. Between 1979 and 2023, we included 2228 frontal and lateral facial photographs corresponding to 541 patients. In all, 70.2% [0.593–0.797] (p &lt; 0.001) of patients in the validation set were correctly diagnosed. Genotypes linked to a splice donor site of FGFR2 in Crouzon-Pfeiffer syndrome (CPS) caused a milder phenotype in CPS. Here we report a new method for the automatic detection of syndromic craniosynostoses using AI.</p

The genetic landscape and clinical spectrum of nephronophthisis and related ciliopathies

Nephronophthisis (NPH) is an autosomal-recessive ciliopathy representing one of the most frequent causes of kidney failure in childhood characterized by a broad clinical and genetic heterogeneity. Applied to one of the worldwide largest cohorts of patients with NPH, genetic analysis encompassing targeted and whole exome sequencing identified disease-causing variants in 600 patients from 496 families with a detection rate of 71%. Of 788 pathogenic variants, 40 known ciliopathy genes were identified. However, the majority of patients (53%) bore biallelic pathogenic variants in NPHP1. NPH-causing gene alterations affected all ciliary modules defined by structural and/or functional subdomains. Seventy six percent of these patients had progressed to kidney failure, of which 18% had an infantile form (under five years) and harbored variants affecting the Inversin compartment or intraflagellar transport complex A. Forty eight percent of patients showed a juvenile (5-15 years) and 34% a late-onset disease (over 15 years), the latter mostly carrying variants belonging to the Transition Zone module. Furthermore, while more than 85% of patients with an infantile form presented with extra-kidney manifestations, it only concerned half of juvenile and late onset cases. Eye involvement represented a predominant feature, followed by cerebellar hypoplasia and other brain abnormalities, liver and skeletal defects. The phenotypic variability was in a large part associated with mutation types, genes and corresponding ciliary modules with hypomorphic variants in ciliary genes playing a role in early steps of ciliogenesis associated with juvenile-to-late onset NPH forms. Thus, our data confirm a considerable proportion of late-onset NPH suggesting an underdiagnosis in adult chronic kidney disease

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes

IntroductionMandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.MethodsThe training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.ResultsWe trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] (p &lt; 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648–0.920] (p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544–0.960] (p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses).ConclusionThis is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers

Using Deep Learning to Improve Phenotyping from Clinical Reports

International audienceWith the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children’s hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task. The solution we devised will help build more reliable decision support systems

Phenotypic similarity for rare disease: Ciliopathy diagnoses and subtyping

International audienceRare diseases are often hard and long to be diagnosed precisely, and most of them lack approved treatment. For some complex rare diseases, precision medicine approach is further required to stratify patients into homogeneous subgroups based on the clinical, biological or molecular features. In such situation, deep phenotyping of these patients and comparing their profiles based on subjacent similarities are thus essential to help fast and precise diagnoses and better understanding of pathophysiological processes in order to develop therapeutic solutions. In this article, we developed a new pipeline of using deep phenotyping to define patient similarity and applied it to ciliopathies, a group of rare and severe diseases caused by ciliary dysfunction. As a French national reference center for rare and undiagnosed diseases, the Necker-Enfants Malades Hospital (Necker Children's Hospital) hosts the Imagine Institute, a research institute focusing on genetic diseases. The clinical data warehouse contains on one hand EHR data, and on the other hand, clinical research data. The similarity metrics were computed on both data sources, and were evaluated with two tasks: diagnoses with EHRs and subtyping with ciliopathy specific research data. We obtained a precision of 0.767 in the top 30 most similar patients with diagnosed ciliopathies. Subtyping ciliopathy patients with phenotypic similarity showed concordances with expert knowledge. Similarity metrics applied to rare disease offer new perspectives in a translational context that may help to recruit patients for research, reduce the length of the diagnostic journey, and better understand the mechanisms of the disease

Aberration-free laser beam in the soft x-ray range

International audienceBy seeding an optical-field-ionized population-inverted plasma amplifier with the 25th harmonic of an IR laser, we have achieved what we believe to be the first aberration-free laser beam in the soft x-ray spectral range. This laser emits within a cone of 1.34 mrad(1/e^2) at a repetition rate of 10 Hz at a central wavelength of 32.8 nm. The beam exhibits a circular profile and wavefront distortions as low as λ/17. A theoretical analysis of these results shows that this high beam quality is due to spatial filtering of the seed beam by the plasma amplifier aperture

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes

International audienceIntroduction Mandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes. Methods The training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set. Results We trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] ( p &lt; 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648–0.920] ( p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544–0.960] ( p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses). Conclusion This is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers

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

Abstract 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

Assessing the impact of environmental factors on plant architecture through an integrative approach

National audiencePlant architecture determines yield, vigour, pathogen resistance of a crop as well as shape and visual quality of plants. Controlling the establishment of plant architecture is therefore a key concern for plant breeders and horticultural growers of field and greenhouse crops. Environmental factors have a strong impact on plant architecture. Better understanding and controlling these factors should allow better mastering cultural practices and increase yield but also reduce the use of chemicals (pesticide and growth retardants). In the case of ornamental crops, this may contribute to better master plant shape and offer the way to create new products. However, the understanding of how the environment modulates plant architecture is still poor and further research is needed. To address this question, ARCH-E (Architecture and Environment) team of the Research Institute on Horticulture and Seeds (IRHS, Angers, France) is developing an integrative research program whereby environmental effects on the establishment of plant architecture are examined from the molecular to the all plant levels. Rosebush is the model plant studied in this program. Architectural analysis is used to describe and objectively discriminate plant shapes (Morel et al., 2009, Chéné et al; 2012) and the impact of environmental factors, such as quantity and quality of light, nitrogen or water restriction or mechanical stimulation on the architectural components is studied (Thélier et al., 2011, Abidi et al; 2012, Morel et al., 2012). Beside, tools to assess plant shape through sensory analysis are developed and used to train panels of assessors to characterize the rosebush visual quality (Boumaza et al; 2010). The more in-depth study of the effect of light on rose architecture is carried on and has revealed that light was essential to bud outgrowth in rose, and that blue or red lights could, each individually, trigger bud burst (Girault et al; 2008). Light was shown to be required to stimulate sugar transport (Henry et al; 2011), sugar metabolism (Girault et al, 2010) and sugar signaling (Rabot et al., 2012) as well as the synthesis of the plant hormone gibberellic acid (Choubane et al ; submitted). On the basis of these researches, functional and structural modeling is undergone to integrate these results and simulate branching in response to the light environment (Bertheloot et al., 2011