Development of machine learning system for airway prediction from facial image with mobile device

Goals: A reliable prognostic tool for a difficult airway (DA) may enhance patients’ safety during orotracheal intubation by decreasing unanticipated DAs. We aim to examine the applicability of an Artificial Intelligence-Deep Learning (AI-DL) algorithm to measure airway’s anatomy, and to predict DA based on published models. Materials and methods: Observational prospective cohort study with n=503 patients recruited at Galdakao-Usansolo and Basurto University Hospitals (Biscay, Spain) between 2018 and 2020. Two pre-operative photos for each patient were collected: a frontal view, in which patients were instructed to open their mouth completely; and a lateral view, with head in vertical ex tension. Smartphones with general-purpose cameras were used, and a cue card was added to the scene as reference. Patients’ medical records were logged. After intubation, HAN score and IDS-ASA criteria for intubation difficulty [1] were collected. Our anaesthesiology team defined a set of relevant orofacial landmarks, whereas our data-science team developed an AI-DL algorithm, trained to identify locate them automatically within the images. In a previous evaluation, the system achieved an accuracy comparable to the consensus of two human annotators [2]. Landmark positions output by the AI-DL method were subsequently used by the system to ex tract two anatomical measurements: thyromental distance and interincisor gap. Finally, these two were integrated into a published model for DA prognosis: Naguib et al. 2006 [3], which also employed patients’ height and Mallampati score. Results and discussion: The estimated incidence of DA was 6.36% (32 out of 503 patients) according to the IDS-ASA criteria. Naguib’s model, when used in combination with our automatic AI-DL based measurements, achieved 53.12% sensitivity and 79.83% specificity; compared to clinicians’ subjective assessment, who obtained 25.00% sensitivity and 93.63% specificity. Conclusion(s): In this work, we evaluated an AI-DL method to predict DA for intubation, with two pre-operative photos and Naguib’s model. Our results complemented expert judgements’ predictive ability in terms of sensitivity, substantially lowering false negatives; at the expense of a restrained loss in specificity (false positives). Thus, our proposal may provide anaesthesiologists with an automatic, objective and accessible decision support tool for the prognosis of DAs

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks

Background:A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients’ morphology. Objective:To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology. Methods:We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists. We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x, y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models. Results:With annotators’ consensus as the ‘gold standard’, our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10-3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982]. In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10-3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans. The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view. Conclusions:We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.BERC.2022-2025 BCAM Severo Ochoa accreditation CEX2021-001142-S / MICIN / AEI / 10.13039/50110001103

Desarrollo de sistema de Machine Learning para la prediccion de vía aérea a partir de imagen facial con dispositivo movil

El manejo de una vía aérea difícil (VAD) representa aún una causa importante de lesiones relacionadas con la anestesia, cuyas complicaciones son potencialmente mortales. El notable interés en la predicción de VAD ha provocado el desarrollo de modelos de predicción, algunos de los cuales ya incluyen algoritmos de Inteligencia Artificial a partir de imágenes. Se realizó un estudio observacional, de cohortes prospectivo, en el que se tomaron imágenes de los pacientes sometidos a una anestesia general, recogiendo la información pre-anestésica así como la información post-intubación. Nuestro equipo desarrolló un algoritmo automático de detección de puntos faciales de cara a la toma de medidas de variables ya validadas de predicción de VAD, que se integraron con el modelo predictivo de Naguib. La incidencia estimada de VAD en nuestra muestra de 503 pacientes fue de un 6,36%. La valoración subjetiva (pre-intervención) de los clínicos obtuvo una sensibilidad de 25.00%, con una especificidad de 93.63%. En comparación, nuestra herramienta alcanzó una sensibilidad del 53.12% y una especificidad del 79.83%. El AUC obtenida, o área bajo la curva ROC, fue de 0.680. Integrando nuestro sistema de medición IA-ML con el modelo de Naguib, los resultados muestran que estamos cerca de igualar la capacidad predictiva del clínico. El potencial del análisis facial en la predicción de VAD nos anima a seguir investigando y a desarrollar modelos propios. Creemos que proporcionará al anestesiólogo una herramienta de ayuda en la toma de decisiones automática, objetiva y accesible

Very low frequency Syndromes

Dismorfología, Citogenética y Clínica: Resultados de estudios sobre los datos del ECEMCThe aim of this chapter is to summarize updated knowledge about the clinical characteristics, etiology, genetic and molecular aspects, as well as mechanisms involved in syndromes having very low frequency, in order to promote their better recognition. During the last five years, a total of 30 syndromes have been published in this chapter of the Boletín del ECEMC. This issue includes the following selected syndromes: Crouzon, Pfeiffer, Apert, Saethre-Chotzen, Carpenter and Muenke. All share craniosynostosis as the main clinical feature but also present with other birth defects, the most important being limb malformations, specially syndactyly and polydactyly. Over 100 syndromes with craniosynostosis have been described, usually involving multiple sutures, and several of them are associated with limb malformations. The clinical overlapping between those syndromes makes difficult to perform a neonatal diagnosis, based on their clinical findings. However, molecular genetic testing, specifically of the FRGR1-3 and TWIST1 genes, could help to establish the diagnosis of some of them. Early diagnosis is important for establishing the most suitable treatment for each patient, as well as to offer an accurate genetic counselling and the possibility of preimplantational and/or prenatal diagnosis.N

Un examen actualizado de la percepción de las barreras para la implementación de la farmacogenómica y la utilidad de los pares fármaco/gen en América Latina y el Caribe

La farmacogenómica (PGx) se considera un campo emergente en los países en desarrollo. La investigación sobre PGx en la región de América Latina y el Caribe (ALC) sigue siendo escasa, con información limitada en algunas poblaciones. Por lo tanto, las extrapolaciones son complicadas, especialmente en poblaciones mixtas. En este trabajo, revisamos y analizamos el conocimiento farmacogenómico entre la comunidad científica y clínica de ALC y examinamos las barreras para la aplicación clínica. Realizamos una búsqueda de publicaciones y ensayos clínicos en este campo en todo el mundo y evaluamos la contribución de ALC. A continuación, realizamos una encuesta regional estructurada que evaluó una lista de 14 barreras potenciales para la aplicación clínica de biomarcadores en función de su importancia. Además, se analizó una lista emparejada de 54 genes/fármacos para determinar una asociación entre los biomarcadores y la respuesta a la medicina genómica. Esta encuesta se comparó con una encuesta anterior realizada en 2014 para evaluar el progreso en la región. Los resultados de la búsqueda indicaron que los países de América Latina y el Caribe han contribuido con el 3,44% del total de publicaciones y el 2,45% de los ensayos clínicos relacionados con PGx en todo el mundo hasta el momento. Un total de 106 profesionales de 17 países respondieron a la encuesta. Se identificaron seis grandes grupos de obstáculos. A pesar de los continuos esfuerzos de la región en la última década, la principal barrera para la implementación de PGx en ALC sigue siendo la misma, la "necesidad de directrices, procesos y protocolos para la aplicación clínica de la farmacogenética/farmacogenómica". Las cuestiones de coste-eficacia se consideran factores críticos en la región. Los puntos relacionados con la reticencia de los clínicos son actualmente menos relevantes. Según los resultados de la encuesta, los pares gen/fármaco mejor clasificados (96%-99%) y percibidos como importantes fueron CYP2D6/tamoxifeno, CYP3A5/tacrolimus, CYP2D6/opioides, DPYD/fluoropirimidinas, TMPT/tiopurinas, CYP2D6/antidepresivos tricíclicos, CYP2C19/antidepresivos tricíclicos, NUDT15/tiopurinas, CYP2B6/efavirenz y CYP2C19/clopidogrel. En conclusión, aunque la contribución global de los países de ALC sigue siendo baja en el campo del PGx, se ha observado una mejora relevante en la región. La percepción de la utilidad de las pruebas PGx en la comunidad biomédica ha cambiado drásticamente, aumentando la concienciación entre los médicos, lo que sugiere un futuro prometedor en las aplicaciones clínicas de PGx en ALC.Pharmacogenomics (PGx) is considered an emergent field in developing countries. Research on PGx in the Latin American and the Caribbean (LAC) region remains scarce, with limited information in some populations. Thus, extrapolations are complicated, especially in mixed populations. In this paper, we reviewed and analyzed pharmacogenomic knowledge among the LAC scientific and clinical community and examined barriers to clinical application. We performed a search for publications and clinical trials in the field worldwide and evaluated the contribution of LAC. Next, we conducted a regional structured survey that evaluated a list of 14 potential barriers to the clinical implementation of biomarkers based on their importance. In addition, a paired list of 54 genes/drugs was analyzed to determine an association between biomarkers and response to genomic medicine. This survey was compared to a previous survey performed in 2014 to assess progress in the region. The search results indicated that Latin American and Caribbean countries have contributed 3.44% of the total publications and 2.45% of the PGx-related clinical trials worldwide thus far. A total of 106 professionals from 17 countries answered the survey. Six major groups of barriers were identified. Despite the region’s continuous efforts in the last decade, the primary barrier to PGx implementation in LAC remains the same, the “need for guidelines, processes, and protocols for the clinical application of pharmacogenetics/pharmacogenomics”. Cost-effectiveness issues are considered critical factors in the region. Items related to the reluctance of clinicians are currently less relevant. Based on the survey results, the highest ranked (96%–99%) gene/drug pairs perceived as important were CYP2D6/tamoxifen, CYP3A5/tacrolimus, CYP2D6/opioids, DPYD/fluoropyrimidines, TMPT/thiopurines, CYP2D6/tricyclic antidepressants, CYP2C19/tricyclic antidepressants, NUDT15/thiopurines, CYP2B6/efavirenz, and CYP2C19/clopidogrel. In conclusion, although the global contribution of LAC countries remains low in the PGx field, a relevant improvement has been observed in the region. The perception of the usefulness of PGx tests in biomedical community has drastically changed, raising awareness among physicians, which suggests a promising future in the clinical applications of PGx in LAC

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

4to. Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad. Memoria académica

Este volumen acoge la memoria académica de la Cuarta edición del Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad, CITIS 2017, desarrollado entre el 29 de noviembre y el 1 de diciembre de 2017 y organizado por la Universidad Politécnica Salesiana (UPS) en su sede de Guayaquil. El Congreso ofreció un espacio para la presentación, difusión e intercambio de importantes investigaciones nacionales e internacionales ante la comunidad universitaria que se dio cita en el encuentro. El uso de herramientas tecnológicas para la gestión de los trabajos de investigación como la plataforma Open Conference Systems y la web de presentación del Congreso http://citis.blog.ups.edu.ec/, hicieron de CITIS 2017 un verdadero referente entre los congresos que se desarrollaron en el país. La preocupación de nuestra Universidad, de presentar espacios que ayuden a generar nuevos y mejores cambios en la dimensión humana y social de nuestro entorno, hace que se persiga en cada edición del evento la presentación de trabajos con calidad creciente en cuanto a su producción científica. Quienes estuvimos al frente de la organización, dejamos plasmado en estas memorias académicas el intenso y prolífico trabajo de los días de realización del Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad al alcance de todos y todas