vol. 4, nº 5, september 2017

Virtual Planning and Intraoperative Navigation in Craniomaxillofacial Surgery

2021-09-06T11:40:49Z

Virtual Planning and Intraoperative Navigation in Craniomaxillofacial Surgery Cebrian Carretero, Jose Luis; Guiñales, Jorge; Burgueño García, Miguel Surgery planning assisted by computer represents one important example of the collaboration between surgeons and engineers. Virtual planning allows surgeons to pre-do the surgery by working over a virtual 3D model of the patient obtained through a computer tomography. Through surgical navigation, surgeons are helped while working with deep structures and can check if they are following accurately the surgical plan. These assistive tools are crucial in the field of facial reconstructive surgery. This paper describes two cases, one related to orbital fractures and another one related to oncological patients, showing the advantages that these tools provide, specifically when used for craniomaxillofacial surgery. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T11:40:49Z No. of bitstreams: 1 ijimai20174_5_12_pdf_10848.pdf: 820447 bytes, checksum: 880ee80e4f70548f734c954ae242ab62 (MD5); Made available in DSpace on 2021-09-06T11:40:49Z (GMT). No. of bitstreams: 1 ijimai20174_5_12_pdf_10848.pdf: 820447 bytes, checksum: 880ee80e4f70548f734c954ae242ab62 (MD5)

Influence of Lymphocyte T CD4 Levels on the Neuropsychological Performance of Population Affected by HIV and with a Previous History of Substance Use

2023-03-09T15:48:27Z

Influence of Lymphocyte T CD4 Levels on the Neuropsychological Performance of Population Affected by HIV and with a Previous History of Substance Use Vázquez-Justo, Enrique; García-Torres, Amalia; Vergara-Moragues, Esperanza The immunological markers help to know if there is a good recovery of the immunological system in patients infected with HIV. Among them, the lymphocyte T CD4 rate is the main indicator of the patient’s immunological state being used for staging HIV infection, evaluating the mortality or comorbidity risk and the vulnerability to certain oportunistic infections. However, its link with the presence of cognitive alterations is not clear. Therefore, the aim of this article is to study if lymphocyte T CD4 levels are connected with the neuropsychological performance of a group of people infected with HIV and with a previous history of substance use. The sample consisted of 80 seropositive males with a previous history of substance use. They were evaluated by means of a neuropsychological battery which assesses the most affected cognitive domains in HIV population. The results showed that the patients having a higher level of immunodeficiency (CD4 <200/ mm3) have a poorer performance in terms of attention, visuomotor dexterity, visual memory, visual perception, auditory-verbal learning and inhibition. Therefore, our results show a realtion between the lymphocyte T CD4 rate and the neuropsychological performance in seropositive people with a previsous history of substance use. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T11:18:29Z No. of bitstreams: 1 ijimai20174_5_11_pdf_11000.pdf: 408431 bytes, checksum: 0318313c2142559347edb713c73c8ecc (MD5); Made available in DSpace on 2021-09-06T11:18:29Z (GMT). No. of bitstreams: 1 ijimai20174_5_11_pdf_11000.pdf: 408431 bytes, checksum: 0318313c2142559347edb713c73c8ecc (MD5)

Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia

2021-09-06T11:13:36Z

Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia Bijalwan, Vishwanath; Balodhi, Meenu; Bagwari, Pragya; Saxena, Bhavya Leukemia is a fast growing cancer also called as blood cancer. It normally originates near bone marrow. The need for automatic leukemia detection system rises ever since the existing working methods include labor-intensive inspection of the blood marking as the initial step in the direction of diagnosis. This is very time consuming and also the correctness of the technique rest on the worker’s capability. This paper describes few image segmentation and feature extraction methods used for leukemia detection. Analyzing through images is very important as from images; diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipment. The system will focus on white blood cells disease, leukemia. Changes in features will be used as a classifier input. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T11:13:36Z No. of bitstreams: 1 ijimai20174_5_10_pdf_10782.pdf: 473393 bytes, checksum: 477ecc52a0289460d3eda1637015ab77 (MD5); Made available in DSpace on 2021-09-06T11:13:36Z (GMT). No. of bitstreams: 1 ijimai20174_5_10_pdf_10782.pdf: 473393 bytes, checksum: 477ecc52a0289460d3eda1637015ab77 (MD5)

A Revision of Preventive Web-based Psychotherapies in Subjects at Risk of Mental Disorders

2023-04-20T14:10:11Z

A Revision of Preventive Web-based Psychotherapies in Subjects at Risk of Mental Disorders Sánchez-Gutiérrez, Teresa; Barbeito, Sara; Calvo, Ana For the last years, the impulse of new technologies has overcome the traditional pathways of face-to-face clinical intervention and web-based psychological methodologies for intervention have started to gain success. This study aims to review the state-of-art about the effectiveness studies on preventive web- based interventions accomplished in samples of subjects at high risk for depressive, anxiety, eating behavior, problematic substance use symptoms and promotion of psychological well-being. Results showed that web-based psychological interventions for the prevention of mental disorders seemed to be effective for at risk individuals. Online health promotion in the general population was also effective to avoid the onset of clinical psychological circumstances. Future research should focus on personalized online intervention and on the evaluation of web-based engagement. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T10:58:29Z No. of bitstreams: 1 ijimai20174_5_9_pdf_14588.pdf: 365947 bytes, checksum: da787637c2959697525955c2c4939dcb (MD5); Made available in DSpace on 2021-09-06T10:58:29Z (GMT). No. of bitstreams: 1 ijimai20174_5_9_pdf_14588.pdf: 365947 bytes, checksum: da787637c2959697525955c2c4939dcb (MD5)

Diagnosis of Malignant Melanoma of Skin Cancer Types

2021-09-06T10:50:21Z

Diagnosis of Malignant Melanoma of Skin Cancer Types Alasadi, Abbas Hassin; Alsafy, Baidaa Malignant melanoma is a kind of skin cancer that begins in melanocytes. It can influence on the skin only, or it may expand to the bones and organs. It is less common, but more serious and aggressive than other types of skin cancer. Malignant Melanoma can happen anywhere on the skin, but it is widespread in certain locations such as the legs in women, the back and chest in men, the face, the neck, mouth, eyes, and genitals. In this paper, a proposed algorithm is designed for diagnosing malignant melanoma types by using digital image processing techniques. The algorithm consists of four steps: preprocessing, separation, features extraction, and diagnosis. A neural network (NN) used to diagnosis malignant melanoma types. The total accuracy of the neural network was 100% for training and 93% for testing. The evaluation of the algorithm is done by using sensitivity, specificity, and accuracy. The sensitivity of NN in diagnosing malignant melanoma types was 95.6%, while the specificity was 92.2% and the accuracy was 93.9%. The experimental results are acceptable. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T10:50:21Z No. of bitstreams: 1 ijimai20174_5_8_pdf_89993.pdf: 1034684 bytes, checksum: 9329f73e3f37b6721b688b3fc500299c (MD5); Made available in DSpace on 2021-09-06T10:50:21Z (GMT). No. of bitstreams: 1 ijimai20174_5_8_pdf_89993.pdf: 1034684 bytes, checksum: 9329f73e3f37b6721b688b3fc500299c (MD5)

Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors

2021-09-06T10:29:42Z

Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors Asawa, Krishna; Gargava, Parth A Brain Computer Interface (BCI) is developed to navigate a micro-controller based robot using Emotiv sensors. The BCI system has a pipeline of 5 stages- signal acquisition, pre-processing, feature extraction, classification and CUDA inter- facing. It shall aid in serving a prototype for physical movement of neurological patients who are unable to control or operate on their muscular movements. All stages of the pipeline are designed to process bodily actions like eye blinks to command navigation of the robot. This prototype works on features learning and classification centric techniques using support vector machine. The suggested pipeline, ensures successful navigation of a robot in four directions in real time with accuracy of 93 percent. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T10:29:42Z No. of bitstreams: 1 ijimai20174_5_7_pdf_15281.pdf: 382642 bytes, checksum: db4209eb609820684284ad3d36af1bbb (MD5); Made available in DSpace on 2021-09-06T10:29:42Z (GMT). No. of bitstreams: 1 ijimai20174_5_7_pdf_15281.pdf: 382642 bytes, checksum: db4209eb609820684284ad3d36af1bbb (MD5)

Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network

2021-09-06T10:21:55Z

Selecting Statistical Characteristics of Brain Signals to Detect Epileptic Seizures using Discrete Wavelet Transform and Perceptron Neural Network Esmaeilpour, Mansour; Abbasi, Rezvan Electroencephalogram signals (EEG) have always been used in medical diagnosis. Evaluation of the statistical characteristics of EEG signals is actually the foundation of all brain signal processing methods. Since the correct prediction of disease status is of utmost importance, the goal is to use those models that have minimum error and maximum reliability. In anautomatic epileptic seizure detection system, we should be able to distinguish between EEG signals before, during and after seizure. Extracting useful characteristics from EEG data can greatly increase the classification accuracy. In this new approach, we first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform(DWT) and then we derive statistical characteristics such as maximum, minimum, average and standard deviation for each sub-band. A multilayer perceptron (MLP)neural network was used to assess the different scenarios of healthy and seizure among the collected signal sets. In order to assess the success and effectiveness of the proposed method, the confusion matrix was used and its accuracy was achieved98.33 percent. Due to the limitations and obstacles in analyzing EEG signals, the proposed method can greatly help professionals experimentally and visually in the classification and diagnosis of epileptic seizures. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T10:21:55Z No. of bitstreams: 1 ijimai20174_5_6_pdf_12024.pdf: 1856037 bytes, checksum: f270cd3180ae7d780c3f2fe4ed7f02f3 (MD5); Made available in DSpace on 2021-09-06T10:21:55Z (GMT). No. of bitstreams: 1 ijimai20174_5_6_pdf_12024.pdf: 1856037 bytes, checksum: f270cd3180ae7d780c3f2fe4ed7f02f3 (MD5)

Masticatory System Biomechanical Photoelastic Simulation fot the Comparision of the Conventional and Uni-Lock Systems in Mandibular Osteosynthesis

2021-09-06T09:59:00Z

Masticatory System Biomechanical Photoelastic Simulation fot the Comparision of the Conventional and Uni-Lock Systems in Mandibular Osteosynthesis Cebrian Carretero, Jose Luis; Carrascal Morillo, María Teresa; Vincent Fraile, Germán The biomechanical consequences of the interaction between titanium trauma plates and screws and the fractured mandible are still a matter of investigation. The mathematical and biomechanical models that have been developed show limitations and the experimental studies are not able to reproduce muscle forces and internal stress distributions in the bone-implant interface and mandibular structure. In the present article we show a static simulator of the masticatory system to demonstrate in epoxy resin mandibular models, by means of 3D (three-dimensional) photoelasticity, the stress distribution using different osteosynthesis methods in the mandibular angle fractures. The results showed that the simulator and 3D photoelasticity were a useful method to study interactions between bone and osteosynthesis materials. The “Lock” systems can be considered the most favourable method due to their stress distribution in the epoxy resin mandible. 3D photoelasticity in epoxy resin models is a useful method to evaluate stress distribution for biomechanical studies. Regarding to mandibular osteosynthesis, “lock” plates offer the most favourable stress distribution due to being less aggressive to the bone Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T09:59:00Z No. of bitstreams: 1 ijimai20174_5_5_pdf_31565.pdf: 1098548 bytes, checksum: 66a87d3f8f0f38c91118b597bc46aa0a (MD5); Made available in DSpace on 2021-09-06T09:59:00Z (GMT). No. of bitstreams: 1 ijimai20174_5_5_pdf_31565.pdf: 1098548 bytes, checksum: 66a87d3f8f0f38c91118b597bc46aa0a (MD5)

Contour Detection of Mammogram Masses Using ChanVese Model and B-Spline Approximation

2021-09-06T09:36:20Z

Contour Detection of Mammogram Masses Using ChanVese Model and B-Spline Approximation Youssef, Youssef Ben; El Abdelmounim, hassane; Lamnii, Abdellah ChanVese model segmentation has been applied for contour detection of mass region in mammogram in our previous work. Available information of the desired object contour is used, in this paper, for B-spline approximation. The mass region boundary (contour) is thereafter approximated by a B-spline curve. This approach allows synthesizing the shape of the suspected mass appearing in the mammogram. Experimental results show the accurateness of mass region contour in mammograms using B-spline approximation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T09:36:20Z No. of bitstreams: 1 ijimai20174_5_4_pdf_17629.pdf: 445945 bytes, checksum: f613634a22df96d931dd324f8c843f9a (MD5); Made available in DSpace on 2021-09-06T09:36:20Z (GMT). No. of bitstreams: 1 ijimai20174_5_4_pdf_17629.pdf: 445945 bytes, checksum: f613634a22df96d931dd324f8c843f9a (MD5)

The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM

2021-09-06T09:14:10Z

The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM Esmaeilpour, Mansour; Gohariyan, Elham; Shirmohammadi, Mohammad Mehdi Breast cancer is one of the common cancers among women so that early diagnosing of it can effectively help its treatment in this study, considering combination of Mammography and MRI pictures, we will try to recognize glands in existing pictures which identify all around of gland complete and precisely and separate it completely. In this method using artificial intelligence algorithm such as Affine transformation, Gabor filter, neural network, and support vector machine, image analysis will be carried out. The accuracy of proposed method is 98.14. In this work a special framework is presented which simplifies cancer diagnosis. The algorithm of proposed method is tested on z16 images. High speed and lack of human error are the most important factors in proposed intelligent system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T09:14:10Z No. of bitstreams: 1 ijimai20174_5_3_pdf_18248.pdf: 963291 bytes, checksum: d0b6e3d2a34a55c062a7031684d754cb (MD5); Made available in DSpace on 2021-09-06T09:14:10Z (GMT). No. of bitstreams: 1 ijimai20174_5_3_pdf_18248.pdf: 963291 bytes, checksum: d0b6e3d2a34a55c062a7031684d754cb (MD5)

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

2021-09-06T09:01:36Z

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation Rezaie, Afsaneh Abdollahzadeh; Habiboghli, Ali In the present paper, a method for the detection of malignant and benign tumors on the CT scan images has been proposed. In the proposed method, firstly the area of interest in which the tumor may exist is selected on the original image and by the use of image segmentation and determination of the image’s threshold limit, the tumor’s area is specified and then edge detection filters are used for detection of the tumor’s edge. After detection of area and by calculating the fractal dimensions with less percent of errors and better resolution, the areas where contain the tumor are determined. The images used in the proposed method have been extracted from cancer imaging archive database that is made available for public. Compared to other methods, our proposed method recognizes successfully benign and malignant tumors in all cases that have been clinically approved and belong to the database. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T09:01:36Z No. of bitstreams: 1 ijimai20174_5_2_pdf_65326.pdf: 695612 bytes, checksum: 11006db3211ec94a20264c13bf2a514e (MD5); Made available in DSpace on 2021-09-06T09:01:36Z (GMT). No. of bitstreams: 1 ijimai20174_5_2_pdf_65326.pdf: 695612 bytes, checksum: 11006db3211ec94a20264c13bf2a514e (MD5)

Difusion-Weighted MRI: from Brownian Motion to Head&Neck Tumor Characterization

2021-09-06T08:20:12Z

Difusion-Weighted MRI: from Brownian Motion to Head&Neck Tumor Characterization Utrilla Contreras, Cristina; Buitrago Sánchez, Nelson Mauricio; Graessner, Joachim; García Raya, Pilar; Marin Aguilera, Begoña This paper describes basic physics as well as clinical applications of diffusion-weighted magnetic resonance imaging. This is a technique that provides complementary information to conventional imaging sequences and it is applied in the field of oncologic imaging. This paper focuses on its specific application in head and neck, mainly in cancer patients, for characterization of primary tumors, and also for monitoring and predicting treatment response after chemotherapy or radiation therapy. Last, although diffusion-weighted imaging is shown to add value in several areas by being part of the multi-parametric magnetic resonance imaging approach, there are some unsolved challenges, which are proposed as future work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T08:20:12Z No. of bitstreams: 1 ijimai20174_5_1_pdf_76297.pdf: 2676778 bytes, checksum: cf31a69e09ed2ebd24c205d9a6a402cd (MD5); Made available in DSpace on 2021-09-06T08:20:12Z (GMT). No. of bitstreams: 1 ijimai20174_5_1_pdf_76297.pdf: 2676778 bytes, checksum: cf31a69e09ed2ebd24c205d9a6a402cd (MD5)

IJIMAI Editor's Note - Vol. 4 Issue 5

2021-09-06T07:51:11Z

IJIMAI Editor's Note - Vol. 4 Issue 5 Cebrian Carretero, Jose Luis Although Medicine has always been considered a Health Science, today it is not possible to obviate its relationship with other disciplines as Humanities and Basic Sciences. Doctors from everywhere and everyday work with the most sophisticated technology are trying to make their profession more accurate and precise, taking in consideration, at the same time, the human part of their daily labour. In this volume of the Journal, we will try to explore the relation between different medical specialities, basic science and engineering. In fact, modern Medicine requires the participation of these professionals who are involved with doctors in multidisciplinary teams. In this sense, Medical Engineering, is a new degree that is offered in a vast number of Universities along the world. This relationship between Medicine and Sciences can be found in any medical speciality so that, our aim in this volume, is to show different examples of doctors working together with other scientifics in any area of Medical sciences.The volume consists on twelve papers. Each paper explores a particular area of this multidisciplinary approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-06T07:51:11Z No. of bitstreams: 1 ijimai20174_5_0_pdf_10979.pdf: 107565 bytes, checksum: 734a030c3e6030ccaec14e2a06369738 (MD5); Made available in DSpace on 2021-09-06T07:51:11Z (GMT). No. of bitstreams: 1 ijimai20174_5_0_pdf_10979.pdf: 107565 bytes, checksum: 734a030c3e6030ccaec14e2a06369738 (MD5)