Scientific Publications

@article{CIDMEJIAS2021105958,

title = {A deep learning approach using synthetic images for segmenting and estimating 3D orientation of nanoparticles in EM images},

author = {A Cid-Mejías and R Alonso-Calvo and H Gavilán and J Crespo and V Maojo},

editor = {Elsevier},

url = {https://www.sciencedirect.com/science/article/pii/S016926072100033X},

doi = {https://doi.org/10.1016/j.cmpb.2021.105958},

issn = {0169-2607},

year  = {2021},

date = {2021-04-01},

journal = {Computer Methods and Programs in Biomedicine},

volume = {202},

abstract = {Background and objective: Nanoparticles present properties that can be applied to a wide range of fields such as biomedicine, electronics or optics. The type of properties depends on several characteristics, being some of them related with the particle structure. A proper characterization of nanoparticles is crucial since it could affect their applications. To characterize a particle shape and size, the nanotechnologists employ Electron Microscopy (EM) to obtain images of nanoparticles and perform measures over them. This task could be tedious, repetitive and slow, we present a Deep Learning method based on Convolutional Neural Networks (CNNs) to detect, segment, infer orientations and reconstruct microscope images of nanoparticles. Since machine learning algorithms depend on annotated data and there is a lack of annotated datasets of nanoparticles, our work makes use of artificial datasets of images resembling real nanoparticles photographs. Methods: Our work is divided into three tasks. Firstly, a method to create annotated datasets of artificial images resembling Scanning Electron Microscope (SEM). Secondly, two models of convolutional neural networks are trained using the artificial datasets previously generated, the first one is in charge of the detection and segmentation of the nanoparticles while the second one will infer the nanoparticle orientation. Finally, the 3D reconstruction module will recreate in a 3D scene the set of detected particles. Results: We have tested our method with five different shapes of basic nanoparticles: spheres, cubes, ellipsoids, hexagonal discs and octahedrons. An analysis of the reconstructions was conducted by manually comparing each of them with the real images. The results obtained have been promising, the particles are segmented and reconstructed accordingly to their shapes and orientations. Conclusions: We have developed a method for nanoparticle detection and segmentation in microscope images. Moreover, we can also infer an approximation of the 3D orientation of the particles and, in conjunction with the detections, create a 3D reconstruction of the photographs. The novelty of our approximation lies in the dataset used. Instead of using annotated images, we have created the datasets simulating the microscope images by using basic geometrical objects that imitate real nanoparticles.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{HERNANDEZIBARBURU2019189,

title = {ICD-10-CM extension with ICD-9 diagnosis codes to support integrated access to clinical legacy data},

author = {G Hernandez-Ibarburu and D Perez-Rey and E Alonso-Oset and R Alonso-Calvo and [de K Schepper] and L Meloni and B Claerhout},

url = {http://www.sciencedirect.com/science/article/pii/S1386505619301972},

doi = {https://doi.org/10.1016/j.ijmedinf.2019.06.010},

issn = {1386-5056},

year  = {2019},

date = {2019-01-01},

journal = {International Journal of Medical Informatics},

volume = {129},

pages = {189 - 197},

abstract = {Introduction 

ICD is currently the most widely used terminology to code diagnosis and procedures. The transition from ICD-9-CM to ICD-10-CM became effective on October 1, 2015 in US and many other countries. Projects that use this codification for research purposes, requires advanced methods to exploit data with both versions of ICD. Although the General Equivalence Mappings (GEMs), provided by the Centers for Medicare and Medicaid Services, might help to overcome these challenges, their direct use as translation mappings is not possible, mostly due to the further specificity of ICD-10-CM concepts. 

Objective 

We propose a methodology to generate an extended version of ICD-10-CM with selected ICD-9-CM diagnosis codes. 

Methods 

The extension was generated using the GEMs relations between concepts of both terminologies and the hierarchical relations of ICD-10-CM. 

Results 

This extended ICD-10-CM, together with modifications to the mapping of ICD-9-CM concepts that were not inserted, allows the generation of an improved translation of legacy data, raising the number of 1-to-1 correspondences by +13.81%. 

Conclusion 

The extended ICD-10-CM enables the accurate integration of ICD-9-CM and ICD-10-CM diagnosis data into a single terminology. With such analysis of data possible without having to specify both ICD-9-CM and ICD-10-CM separately for each query.},

keywords = {clinical terminologies, component, data integration, ICD-10, ICD-9, interoperability},

pubstate = {published},

tppubtype = {article}

}

@article{HERNANDEZIBARBURU201970,

title = {ICD-10-PCS extension with ICD-9 procedure codes to support integrated access to clinical legacy data},

author = {G Hernandez-Ibarburu and D Perez-Rey and E Alonso-Oset and R Alonso-Calvo and D Voets and C Mueller and B Claerhout and N V Custodix},

url = {http://www.sciencedirect.com/science/article/pii/S1386505618302181},

doi = {https://doi.org/10.1016/j.ijmedinf.2018.11.002},

issn = {1386-5056},

year  = {2019},

date = {2019-01-01},

journal = {International Journal of Medical Informatics},

volume = {122},

pages = {70 - 79},

abstract = {Since the creation of The International Classification of Diseases (ICD), new versions have been released to keep updated with the current medical knowledge. Migrations of Electronic Health Records (EHR) from ICD-9 to ICD-10-PCS as clinical procedure codification system, has been a significant challenge and involved large resources. In addition, it created new barriers for integrated access to legacy medical procedure data (frequently ICD-9 coded) with current data (frequently ICD-10-PCS coded). This work proposes a solution based on extending ICD-10-PCS with a subgroup of ICD-9-CM concepts to facilitate such integrated access. The General Equivalence Mappings (GEMs) has been used as foundation to set the terminology relations of these inserted concepts in ICD-10-PCS hierarchy, but due to the existence of 1-to-many mappings, advanced rules are required to seamlessly integrate both terminologies. With the generation of rules based on GEMs relationships, 2014 ICD-9 concepts were included within the ICD-10-PCS hierarchy. For the rest of the concepts, a new method is also proposed to increase 1-to-1 mappings. As results, with the suggested approach, the percentage of ICD-9-CM procedure concepts that can be mapped accurately (avoiding mappings to a large number of concepts) rise from 11.56% to 69.01% of ICD-9-Proc, through the extended ICD-10-PCS hierarchy.},

keywords = {clinical terminologies, component, data integration, ICD-10, ICD-9, interoperability},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32308839,

title = {genoĐraw: A Web Ŧool for Đeveloping Pedigree Điagrams Using the Standardized Ħuman Pedigree Nomenclature Integrated with Biomedical Vocabularies},

author = {L Garcia-Giordano and S Paraiso-Medina and R Alonso-Calvo and F J Fern?ndez-Mart?nez and V Maojo},

year  = {2019},

date = {2019-01-01},

volume = {2019},

pages = {457--466},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hernandez2018icd,

title = {ICD-10-PCS Extension with ICD-9 Procedure Codes to Support Integrated Access to Clinical Legacy Data},

author = {G Hernandez-Ibarburu and D Perez-Rey and E Alonso-Oset and R Alonso-Calvo and D Voets and C Mueller and B Claerhout},

doi = {https://doi.org/10.1016/j.ijmedinf.2018.11.002},

year  = {2018},

date = {2018-11-16},

journal = {International Journal of Medical Informatics},

abstract = {Since the creation of The International Classification of Diseases (ICD), new versions have been released to keep updated with the current medical knowledge. Migrations of Electronic Health Records (EHR) from ICD-9 to ICD-10-PCS as clinical procedure codification system, has been a significant challenge and involved large resources. In addition, it created new barriers for integrated access to legacy medical procedure data (frequently ICD-9 coded) with current data (frequently ICD-10-PCS coded). This work proposes a solution based on extending ICD-10-PCS with a subgroup of ICD-9-CM concepts to facilitate such integrated access. The General Equivalence Mappings (GEMs) has been used as foundation to set the terminology relations of these inserted concepts in ICD-10-PCS hierarchy, but due to the existence of 1-to-many mappings, advanced rules are required to seamlessly integrate both terminologies. With the generation of rules based on GEMs relationships, 2,014 ICD-9 concepts were included within the ICD-10-PCS hierarchy. For the rest of the concepts, a new method is also proposed to increase 1-to-1 mappings. As results, with the suggested approach, the percentage of ICD-9-CM procedure concepts that can be mapped accurately (avoiding mappings to a large number of concepts) rise from 11.56% to 69.01% of ICD-9-Proc, through the extended ICD-10-PCS hierarchy.},

keywords = {clinical terminologies, component, data integration, ICD-10, ICD-9, interoperability},

pubstate = {published},

tppubtype = {article}

}

@article{pmid28092493b,

title = {Enhancing Collaborative Case Diagnoses Through Unified Medical Language System-Based Disambiguation: A Case Study of the Zika Virus},

author = {A Moreira and R Alonso-Calvo and A Muñoz and J Crespo},

url = {https://www.ncbi.nlm.nih.gov/pubmed/28092493},

year  = {2017},

date = {2017-07-01},

journal = {Telemed J E Health},

volume = {23},

number = {7},

pages = {608--614},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid28982381,

title = {Querying clinical data in HL7 RIM based relational model with morph-RDB},

author = {F Priyatna and R Alonso-Calvo and S Paraiso-Medina and O Corcho},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629785/},

year  = {2017},

date = {2017-01-01},

journal = {J Biomed Semantics},

volume = {8},

number = {1},

pages = {49},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid28802325b,

title = {SNOMED2HL7: A tool to normalize and bind SNOMED CT concepts to the HL7 Reference Information Model},

author = {D Perez-Rey and R Alonso-Calvo and S Paraiso-Medina and C R Munteanu and M Garcia-Remesal},

url = {https://www.ncbi.nlm.nih.gov/pubmed/28802325},

year  = {2017},

date = {2017-01-01},

journal = {Comput Methods Programs Biomed},

volume = {149},

pages = {1--9},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid28601027,

title = {A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer},

author = {R Alonso-Calvo and S Paraiso-Medina and D Perez-Rey and E Alonso-Oset and R van Stiphout and S Yu and V Maojo },

url = {https://www.ncbi.nlm.nih.gov/pubmed/28601027},

year  = {2017},

date = {2017-01-01},

journal = { Computers in Biology and Medicine},

volume = {87},

pages = {179--186},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid27570644,

title = {Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL},

author = {A Bucur and J van Leeuwen and N Z Chen and B Claerhout and K de Schepper and D Perez-Rey and S Paraiso-Medina and R Alonso-Calvo and K Mehta and C Krykwinski},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001740/},

year  = {2016},

date = {2016-01-01},

journal = {AMIA Joint Summits on Translational Science Proceedings},

volume = {2016},

pages = {25--32},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25248204,

title = {Semantic Normalization and Query Abstraction Based on SNOMED-CT and HL7: Supporting Multicentric Clinical Trials},

author = {S Paraiso-Medina and D Perez-Rey and A Bucur and B Claerhout and R Alonso-Calvo},

url = {https://www.ncbi.nlm.nih.gov/pubmed/25248204},

year  = {2015},

date = {2015-05-01},

journal = {IEEE J Biomed Health Inform},

volume = {19},

number = {3},

pages = {1061-1067},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alonso-Calvo2015,

title = {Enabling semantic interoperability in multi-centric clinical trials on breast cancer},

author = {R Alonso-Calvo and D Perez-Rey and S Paraiso-Medina and B Claerhout and P Hennebert and A Bucur},

doi = {https://doi.org/10.1016/j.cmpb.2015.01.003},

year  = {2015},

date = {2015-03-07},

journal = {Computed Methods and Programs in Biomedicine},

volume = {118},

number = {3},

pages = {322-329},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25160302b,

title = {Supporting patient screening to identify suitable clinical trials},

author = {A Bucur and J Van Leeuwen and N Z Chen and B Claerhout and K De Schepper and D Perez-Rey and R Alonso-Calvo and L Pugliano and K Saini},

url = {https://www.ncbi.nlm.nih.gov/pubmed/25160302},

year  = {2014},

date = {2014-01-01},

journal = {Stud Health Technol Inform},

volume = {205},

pages = {823--827},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alonso-Calvo2010,

title = { On distributing load in cloud computing: A real application for very-large image datasets},

author = {R Alonso-Calvo and J Crespo and M Garcia-Remesal and A Anguita and V Maojo},

url = {http://www.sciencedirect.com/science/article/pii/S1877050910003017},

year  = {2010},

date = {2010-05-12},

journal = {Proceedings of International Conference on Computational Science},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alonso-Calvo2007,

title = {An agent- and ontology-based system for integrating public gene, protein, and disease databases},

author = {R Alonso-Calvo and V Maojo and H Billhardt and F Martin-Sánchez and M Garcia-Remesal and D Perez-Rey},

url = {http://www.sciencedirect.com/science/article/pii/S1532046406000323},

year  = {2007},

date = {2007-02-15},

journal = {Journal of Biomedical Informatics},

volume = {40},

number = {1},

pages = {17-29},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid16144697b,

title = {ONTOFUSION: ontology-based integration of genomic and clinical databases},

author = {D Perez-Rey and V Maojo and M Garcia-Remesal and R Alonso-Calvo and H Billhardt and F Martin-Sanchez and A Sousa},

url = {https://www.ncbi.nlm.nih.gov/pubmed/16144697},

year  = {2006},

date = {2006-01-01},

journal = {Comput. Biol. Med.},

volume = {36},

number = {7-8},

pages = {712--730},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid16538285b,

title = {Designing new methodologies for integrating biomedical information in clinical trials},

author = {V Maojo and M Garcia-Remesal and H Billhardt and R Alonso-Calvo and D Perez-Rey and F Martin-Sanchez},

url = {https://www.ncbi.nlm.nih.gov/pubmed/16538285},

year  = {2006},

date = {2006-01-01},

journal = {Methods Inf Med},

volume = {45},

number = {2},

pages = {180--185},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity },

author = {M Intriago-Pazmino and J Ibarra-Fiallo and J Crespo and R Alonso-Calvo},

editor = {SAGE Publications Sage UK: London, England},

url = {https://journals.sagepub.com/doi/full/10.1177/1460458220935369},

doi = {https://doi.org/10.1177%2F1460458220935369},

journal = {Health Informatics Journal},

volume = {26},

number = {4},

pages = {2722-2736},

abstract = {Retinopathy of prematurity is a disease that can affect premature or in similar conditions babies. For diagnosing of retinopathy of prematurity, the infant is examined as soon as possible. Due to the nature of the examination, the images obtained are poor in quality. This article presents an automated method for processing fundus images to improve the visibility of the vascular network. The method includes several processing tasks whose parameters are predicted using an artificial neural network. A set of 88 clinical images were used in this work. The performance of our proposal is efficient, and the average processing time was 42 ms. The method was assessed using both the contrast improvement index and expert opinions. The contrast improvement index average was 2; this means the processed image successfully improved its contrast. Three pediatric ophthalmologists validated the proposed method and agreed that the visual enhancement can help observe clearly the retinal vessels.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}