A comparison of Covid-19 early detection between convolutional neural networks and radiologists

[EN] Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovacion, Generalitat Valenciana.Albiol Colomer, A.; Albiol, F.; Paredes Palacios, R.; Plasencia-Martínez, JM.; Blanco Barrio, A.; García Santos, JM.; Tortajada, S.... (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights into Imaging. 13(1):1-12. https://doi.org/10.1186/s13244-022-01250-311213

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovación, Generalitat Valenciana.Peer reviewe

Whole-Exome Sequencing of 24 Spanish Families: Candidate Genes for Non-Syndromic Pediatric Keratoconus

Keratoconus is a corneal dystrophy that is one of the main causes of corneal transplantation and for which there is currently no effective treatment for all patients. The presentation of this disease in pediatric age is associated with rapid progression, a worse prognosis and, in 15–20% of cases, the need for corneal transplantation. It is a multifactorial disease with genetic variability, which makes its genetic study difficult. Discovering new therapeutic targets is necessary to improve the quality of life of patients. In this manuscript, we present the results of whole-exome sequencing (WES) of 24 pediatric families diagnosed at the University Hospital La Paz (HULP) in Madrid. The results show an oligogenic inheritance of the disease. Genes involved in the structure, function, cell adhesion, development and repair pathways of the cornea are proposed as candidate genes for the disease. Further studies are needed to confirm the involvement of the candidate genes described in this article in the development of pediatric keratoconus

CLAPO syndrome: identification of somatic activating PIK3CA mutations and delineation of the natural history and phenotype

[Purpose]: CLAPO syndrome is a rare vascular disorder characterized by capillary malformation of the lower lip, lymphatic malformation predominant on the face and neck, asymmetry, and partial/generalized overgrowth. Here we tested the hypothesis that, although the genetic cause is not known, the tissue distribution of the clinical manifestations in CLAPO seems to follow a pattern of somatic mosaicism.[Methods]: We clinically evaluated a cohort of 13 patients with CLAPO and screened 20 DNA blood/tissue samples from 9 patients using high-throughput, deep sequencing.[Results]: We identified five activating mutations in the PIK3CA gene in affected tissues from 6 of the 9 patients studied; one of the variants (NM_006218.2:c.248T>C; p.Phe83Ser) has not been previously described in developmental disorders.[Conclusion]: We describe for the first time the presence of somatic activating PIK3CA mutations in patients with CLAPO. We also report an update of the phenotype and natural history of the syndrome.This research was supported by the project “Genetics of vascular and lymphatic malformations” financed with funds donated by Asociación Ultrafondo and Villareal FC, cofinanced by project IP-17 from the funding call “Todos Somos Raros” (Telemaraton TVE promoted by Fundación Isabel Gemio, Federación ASEM, and Federación Española de Enfermedades Raras), cofinanced by the Instituto de Salud Carlos III, FEDER FUNDS FIS PI15/01481, and IIS-Fundación Jiménez Díaz UAM Genome Medicine Chair.Peer reviewe

PIAS4 is associated with macro/microcephaly in the novel interstitial 19p13.3 microdeletion/microduplication syndrome

Array comparative genomic hybridization (aCGH) is a powerful genetic tool that has enabled the identification of novel imbalances in individuals with intellectual disability (ID), autistic disorders and congenital malformations. Here we report a \u27genotype first\u27 approach using aCGH on 13 unrelated patients with 19p13.3 submicroscopic rearrangement (11 deletions and 2 duplications) and review cases in the literature and in public databases. Shared phenotypic features suggest that these patients represent an interstitial microdeletion/microduplication syndrome at 19p13.3. Common features consist of abnormal head circumference in most patients (macrocephaly with the deletions and microcephaly with the duplications), ID with developmental delay (DD), hypotonia, speech delay and common dysmorphic features. The phenotype is associated with at least a ~0.113 Mb critical region harboring three strong candidate genes probably associated with DD, ID, speech delay and other dysmorphic features: MAP2K2, ZBTB7A and PIAS4, an E3 ubiquitin ligase involved in the ubiquitin signaling pathways, which we hypothesize for the first time to be associated with head size in humans