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Silva AJ, de Sousa RJ, Fernandes FA, Ptak M, Dymek M, Parente MP. Improvement and validation of a female finite element model of the cervical spine. J Mech Behav Biomed Mater 2023; 142:105797. [PMID: 37058864 DOI: 10.1016/j.jmbbm.2023.105797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 02/20/2023] [Accepted: 03/23/2023] [Indexed: 04/03/2023]
Abstract
Although the cervical spine supports and controls the kinematics of the head, it is vulnerable to injuries during mechanical loading. Severe injuries often result in damage to the spinal cord, leading to significant ramifications. The role of gender in determining the outcome of such injuries has been established as significant. In order to better understand the essential mechanics and develop treatments or preventative measures, various forms of research have been conducted. Computational modelling is one of the most useful and extensively utilised methods, as it provides information that would otherwise be difficult to obtain. As such, the primary goal of this research is to create a new finite element of the female cervical spine that will more accurately represent the group most affected by such injuries. This work is a continuation of a previous study where a model was created from the computer tomography scans of a 46-year-old female. A functioning spinal unit consisting of the C6-C7 segment was simulated as a validation procedure. The experimental data obtained from cadaveric specimens, that assessed the range of motion of different cervical segments in flexion-extension, axial rotation, and lateral bending, was used to validate the reduced model.
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Pereira P, Mascarenhas M, Ribeiro T, Afonso J, Ferreira JPS, Vilas-Boas F, Parente MP, Jorge RN, Macedo G. Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy. Endosc Int Open 2022; 10:E262-E268. [PMID: 35295246 PMCID: PMC8920599 DOI: 10.1055/a-1723-3369] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/05/2021] [Indexed: 12/15/2022] Open
Abstract
Background and study aims Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, and tortuous vessels, often described as tumor vessels, are frequently reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy (D-SOC). In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of tumor vessels (TVs) in D-SOC images. Patients and methods A convolutional neural network (CNN) was developed. A total of 6475 images from 85 patients who underwent D-SOC (Spyglass, Boston Scientific, Marlborough, Massachusetts, United States) were included. Each frame was evaluated for the presence of TVs. The performance of the CNN was measured by calculating the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Results The sensitivity, specificity, positive predictive value, and negative predictive value were 99.3 %, 99.4 %, 99.6% and 98.7 %, respectively. The AUC was 1.00. Conclusions Our CNN was able to detect TVs with high accuracy. Development of AI algorithms may enhance the detection of macroscopic characteristics associated with high probability of biliary malignancy, thus optimizing the diagnostic workup of patients with indeterminate biliary strictures.
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Affiliation(s)
- Pedro Pereira
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João P. S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Filipe Vilas-Boas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Marco P.L. Parente
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Renato N. Jorge
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto, Porto, Portugal
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Mascarenhas M, Ribeiro T, Afonso J, Ferreira JP, Cardoso H, Andrade P, Parente MP, Jorge RN, Mascarenhas Saraiva M, Macedo G. Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network. Endosc Int Open 2022; 10:E171-E177. [PMID: 35186665 PMCID: PMC8850002 DOI: 10.1055/a-1675-1941] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/21/2021] [Indexed: 10/31/2022] Open
Abstract
Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.
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Affiliation(s)
- Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João P.S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
| | - Marco P.L. Parente
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
| | - Renato N. Jorge
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
| | | | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
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Mascarenhas Saraiva M, Ribeiro T, Afonso J, Ferreira JP, Cardoso H, Andrade P, Parente MP, Jorge RN, Macedo G. Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network. GE Port J Gastroenterol 2021; 29:331-338. [PMID: 36159196 PMCID: PMC9485980 DOI: 10.1159/000518901] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 07/14/2021] [Indexed: 12/22/2022]
Abstract
Introduction Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams. Methods A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision. Results Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s). Discussion/Conclusion We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
- * Miguel Mascarenhas Saraiva,
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João P.S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI − Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Marco P.L. Parente
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI − Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Renato N. Jorge
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI − Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
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Oliveira DA, Parente MP, Calvo B, Mascarenhas T, Natal Jorge RM. Numerical simulation of the damage evolution in the pelvic floor muscles during childbirth. J Biomech 2016; 49:594-601. [DOI: 10.1016/j.jbiomech.2016.01.014] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Revised: 01/13/2016] [Accepted: 01/28/2016] [Indexed: 01/30/2023]
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