Pattern recognition in medical settings

A document is presented with the results of various investigations related to data patterns, more specifically those that have been designed with intelligent computational tools. The use of data patterns in medicine is something that has emerged in recent decades and that increasingly presents development alternatives for engineering projects. Various scientific publications are evaluated in this document to relate engineering applications in medicine, and contrast the possible technological impact offered by computational sciences. Keywords: Engineering in medicine, pattern recognition, computer science. References [1]R. Pallás-Areny, «LA INGENIERÍA ELECTRÓNICA Y LA MEDICINA,» [En línea]. Available: https://www.researchgate.net/profile/Ramon-Pallas-Areny/publication/239813249_La_Ingenieria_electronica_y_la_medicina/links/0deec539fea82baf03000000/La-Ingenieria-electronica-y-la-medicina.pdf. [Último acceso: 27 diciembre 2021].[2]H. Medellín, G. González, R. Espinosa, E. Govea y T. Lim, «Desarrollo de Aplicaciones de Realidad Virtual y Sistemas Hápticos en Ingeniería, medicina y arte,» de Ciencias de la Ingeniería y Tecnología, San Luis Potosí-Mexico, Universidad Autónoma de San Luis Potosí, 2014, pp. 77-93. [3]S. Chris, E. Ray, J. Andrew y L. Jason, «Designing cranial implants in a haptic augmented reality environment,» Communications of the ACM, vol. 47, nº 8, pp. 33-38, 2004. [4]G. Sabine, K. Erwin y G. Bernd, «Advances in interactive craniofacial surgery planning by 3D simulation and visualization.,» Oral and Maxillofacial Surgery, vol. 24, pp. 120-125, 1995. [5]P. Philipp, G. B. Alexander, P. Andreas, V. S. Norman, P. Bernhard, P. Andreas, H. Karl-Heinz, T. Ulf, S. Ingo y H. Max, «Virtual Dental Surgery as a New Educational Tool in Dental School,» Journal of Cranio- Maxillo-Facial Surgery, vol. 38, pp. 560-564, 2010. [6]C. Castañeda y F. Vázquez, «Realidad Virtual, un apoyo en la Terapia de Acrofobia, Claustrofobia y Agorafobia, » de Memorias del VIII Congreso Internacional sobre Innovación y Desarrollo Tecnológico (CIINDET 2011), Cuernavaca Morelos, México., 2011. [7]F. Suárez, O. Flor y L. Rosales, «Sistema de interpretación de conductas para identificación de situaciones de riesgo,» Revista Ibérica de Sistemas e Tecnologias de Informação, vol. E31, pp. 309-317, 2020.

Education for modern engineering

Education has undergone evolutionary changes necessary to be able to generate the necessary contributions for each era, thus creating spaces for discussion that produce new methodologies and new paradigms for teaching. The case of engineering is very particular in these times, and university education should make its best efforts to offer future engineers the necessary skills to face the challenges of modern industry. In this work a literature review is made to analyze the new educational proposals that will be necessary for the training of the engineer in times of industrial digitization. The results show that an adaptation to the teaching processes is necessary, such that an appropriate engineering training is feasible, which assists and meets the requirements of the industry of the future. Keywords: Educational methodologies, modern industry, teaching processes. References [1]La importancia de las letras, «La historia de la educación,» 2010. [Online]. Available: http://historiageneraldelaeducacion.blogspot.com/2010/03/historia-de-la-educacion-conclusion.html. [Last access: 27 11 2021]. [2]V. Guichot, «HISTORIA DE LA EDUCACIÓN: REFLEXIONES SOBRE SU OBJETO, UBICACIÓN EPISTEMOLÓGICA, DEVENIR HISTÓRICO Y TENDENCIAS ACTUALES,» Revista Latinoamericana de Estudios Educativos, vol. 2, nº 1, pp. 11-51, 2006. [3]K. Zambrano, «Línea de tiempo de la historia de la educación,» 13 septiembre 2018. [Online]. Available: https://prezi.com/p/oashlaqm_uxn/linea-del-tiempo-historia-de-la-educacion/. [Last access: 24 11 2021]. [4]M. Begoña Tellería, «Educación y nuevas tecnologías. Educación a Distancia y Educación Virtual,» Revista de Teoría y Didáctica de las Ciencias, nº 9, pp. 209-222, 2004. [5]R. Nieto, «EDUCACIÓN VIRTUAL O VIRTUALIDAD DE LA EDUCACIÓN,» Rev.hist.educ.latinoam, vol.14, nº 19, 2012. [6]R. Pallás-Areny, «LA INGENIERÍA ELECTRÓNICA Y LA MEDICINA,» [Online]. Available: https://www.researchgate.net/profile/Ramon-Pallas-Areny/publication/239813249_La_Ingenieria_electronica_y_la_medicina/links/0deec539fea82baf03000000/La-Ingenieria-electronica-y-la-medicina.pdf. [Last access: 27 12 2021]. [7]H. Medellín, G. González, R. Espinosa, E. Govea and T. Lim, «Desarrollo de Aplicaciones de Realidad Virtual y Sistemas Hápticos en Ingeniería, medicina y arte,» de Ciencias de la Ingeniería y Tecnología, San Luis Potosí- Mexico, Universidad Autónoma de San Luis Potosí, 2014, pp. 77-93. [8]S. Chris, E. Ray, J. Andrew and L. Jason, «Designing cranial implants in a haptic augmented reality environment,»Communications of the ACM, vol. 47, nº 8, pp. 33-38, 2004. [9]G. Sabine, K. Erwin and G. Bernd, «Advances in interactive craniofacial surgery planning by 3D simulation and visualization.,» Oral and Maxillofacial Surgery, vol. 24, pp. 120-125, 1995. [10]P. Philipp, G. B. Alexander, P. Andreas, V. S. Norman, P. Bernhard, P. Andreas, H. Karl-Heinz, T. Ulf, S. Ingo y H. Max, «Virtual Dental Surgery as a New Educational Tool in Dental School,» Journal of Cranio- Maxillo-Facial Surgery, vol. 38, pp. 560-564, 2010. [11]C. Castañeda and F. Vázquez, «Realidad Virtual, un apoyo en la Terapia de Acrofobia, Claustrofobia y Agorafobia, » de Memorias del VIII Congreso Internacional sobre Innovación y Desarrollo Tecnológico (CIINDET 2011), Cuernavaca Morelos, México., 2011. [12]F. Suárez, O. Flor and L. Rosales, «Sistema de interpretación de conductas para identificación de situaciones de riesgo,» Revista Ibérica de Sistemas e Tecnologias de Informação, vol. E31, pp. 309-317, 2020.

Investigating the Reliability of Novel Nasal Anthropometry Using Advanced Three-Dimensional Digital Stereophotogrammetry

Three-dimensional surface imaging systems (3DSI) provide an effective and applicable approach for the quantification of facial morphology. Several researchers have implemented 3D techniques for nasal anthropometry; however, they only included limited classic nasal facial landmarks and parameters. In our clinical routines, we have identified a considerable number of novel facial landmarks and nasal anthropometric parameters, which could be of great benefit to personalized rhinoplasty. Our aim is to verify their reliability, thus laying the foundation for the comprehensive application of 3DSI in personalized rhinoplasty. We determined 46 facial landmarks and 57 anthropometric parameters. A total of 110 volunteers were recruited, and the intra-assessor, inter-assessor, and intra-method reliability of nasal anthropometry were assessed through 3DSI. Our results displayed the high intra-assessor reliability of MAD (0.012–0.29, 0.003–0.758 mm), REM (0.008–1.958%), TEM (0–0.06), rTEM (0.001–0.155%), and ICC (0.77–0.995); inter-assessor reliability of 0.216–1.476, 0.003–2.013 mm; 0.01–7.552%, 0–0.161, and 0.001–1.481%, 0.732–0.985, respectively; and intra-method reliability of 0.006–0.598°, 0–0.379 mm; 0 0.984%, 0–0.047, and 0–0.078%, 0.996–0.998, respectively. This study provides conclusive evidence for the high reliability of novel facial landmarks and anthropometric parameters for comprehensive nasal measurements using the 3DSI system. Considering this, the proposed landmarks and parameters could be widely used for digital planning and evaluation in personalized rhinoplasty, otorhinolaryngology, and oral and maxillofacial surgery.

Automated Prediction of Extraction Difficulty and Inferior Alveolar Nerve Injury for Mandibular Third Molar Using a Deep Neural Network

Extraction of mandibular third molars is a common procedure in oral and maxillofacial surgery. There are studies that simultaneously predict the extraction difficulty of mandibular third molar and the complications that may occur. Thus, we propose a method of automatically detecting mandibular third molars in the panoramic radiographic images and predicting the extraction difficulty and likelihood of inferior alveolar nerve (IAN) injury. Our dataset consists of 4903 panoramic radiographic images acquired from various dental hospitals. Seven dentists annotated detection and classification labels. The detection model determines the mandibular third molar in the panoramic radiographic image. The region of interest (ROI) includes the detected mandibular third molar, adjacent teeth, and IAN, which is cropped in the panoramic radiographic image. The classification models use ROI as input to predict the extraction difficulty and likelihood of IAN injury. The achieved detection performance was 99.0% mAP over the intersection of union (IOU) 0.5. In addition, we achieved an 83.5% accuracy for the prediction of extraction difficulty and an 81.1% accuracy for the prediction of the likelihood of IAN injury. We demonstrated that a deep learning method can support the diagnosis for extracting the mandibular third molar.