A5001239416- Gastrointestinal Bleeding Diagnosis and Treatment
- Colorectal Cancer Screening and Detection
- Gastric Cancer Management and Outcomes
- Pancreatic and Hepatic Oncology Research
- Gallbladder and Bile Duct Disorders
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Pelvic floor disorders treatments
- Gastrointestinal disorders and treatments
- AI in cancer detection
- Cellular Mechanics and Interactions
- Pelvic and Acetabular Injuries
- Elasticity and Material Modeling
- Inflammatory Bowel Disease
- Pregnancy-related medical research
- Radiomics and Machine Learning in Medical Imaging
- Colorectal and Anal Carcinomas
- Esophageal and GI Pathology
- Cardiac tumors and thrombi
- 3D Printing in Biomedical Research
- Sarcoma Diagnosis and Treatment
- Wound Healing and Treatments
- Cervical Cancer and HPV Research
- Force Microscopy Techniques and Applications
- FinTech, Crowdfunding, Digital Finance
- Esophageal Cancer Research and Treatment
Universidade do Porto
2016-2025
Hospital Universitario San Rafael
2024-2025
Institute of Mechanical Engineering and Industrial Mangement
2015-2024
Universidade Federal do ABC
2024
Laboratório Associado de Energia, Transportes e Aeronáutica
2022
National Institute of Diabetes and Digestive and Kidney Diseases
2022
ProQuest (United States)
2022
Hospital de São João
2012-2021
Université Paris-Saclay
2020
Hôpital Ambroise-Paré
2020
Capsule endoscopy is a central element in the management of patients with suspected or known Crohn's disease. In 2017, PillCam™ was introduced and demonstrated to have greater accuracy evaluation extension disease these patients. Artificial intelligence [AI] expected enhance diagnostic capsule endoscopy. This study aimed develop an AI algorithm for automatic detection ulcers erosions small intestine colon images.A total 8085 images were extracted between 2017 2020, comprising 2855 1975...
Bone is an outstanding, well-designed composite. It constituted by a multi-level structure wherein its properties and behavior are dependent on composition structural organization at different length scales. The combination of unique mechanical with adaptive self-healing abilities makes bone innovative model for the future design synthetic biomimetic composites improved performance in repair regeneration. However, relation between very complex. In this review article, we intend to describe...
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 prone errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed develop deep learning model for automatic identification differentiation of significant colonic mucosal lesions blood images. Patients methods A...
The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by lengthy reading times. As a result, there growing interest employing artificial intelligence (AI) these diagnostic therapeutic procedures, driven the prospect overcoming some major limitations enhancing healthcare efficiency, while maintaining high accuracy levels. In past two decades, applicability AI to...
Objective Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the bowel. CE frequently identifies a wide range lesions with different potentials in these patients. However, reading examinations time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools image analysis. This study aims to develop CNN-based model identification and differentiation multiple distinct...
Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for automatic diagnosis of PCLs. Images retrieved from videos EUS examinations PCL characterization were used development, training, validation CNN cyst diagnosis. The performance was measured calculating area under receiving...
The medical community has been focusing on gaining a deeper understanding of birth trauma, which affects millions women worldwide. Maternal lesions can be challenging to diagnose and expensive examine. To better comprehend the mechanism injuries occurring in pelvic floor muscles (PFM), biomechanical simulations valuable tool. However, utilizing finite element method (FEM) conduct time-consuming process. overcome this issue, present study aims develop machine learning (ML) framework predict...
In this work, the analogous treatment between coupled temperature–displacement problems and material failure models is explored within context of a commercial software (Abaqus®). The implicit gradient Lemaitre damage phase field are implemented utilizing underlying capabilities for problems. heat conduction equation made compatible with diffusive regularization such calculations carried out at point level. This bypasses need to implement explicitly weak form resultant from coupling momentum...
Background and study aims Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, tortuous vessels, often described as tumor are frequently reported in 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 develop an AI algorithm automatic detection vessels (TVs) D-SOC...
Capsule endoscopy (CE) is a minimally invasive examination for evaluating the gastrointestinal tract. However, its diagnostic yield detecting gastric lesions suboptimal. Convolutional neural networks (CNNs) are artificial intelligence models with great performance image analysis. Nonetheless, their role in evaluation by wireless CE (WCE) has not been explored.Our group developed CNN-based algorithm automatic classification of pleomorphic lesions, including vascular (angiectasia, varices, and...
Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed develop a convolutional neural network (CNN) for identification and morphological characterization of malignant BSs D-SOC. A total 84,994 images from 129 exams two centers (Portugal Spain) were used developing CNN. Each image was categorized as either...
Capsule endoscopy (CE) is commonly used as the initial exam for suspected mid-gastrointestinal bleeding after normal upper and lower endoscopy. Although assessment of small bowel primary focus CE, detecting upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop test a convolutional neural network (CNN)-based model panendoscopic automatic detection during CE.
Introduction: High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell cancer (ASCC) precursors. Preliminary studies on application of artificial intelligence (AI) models to this modality have revealed promising results. However, impact staining techniques and manipulation effectiveness these algorithms has not been evaluated. We aimed develop a deep learning system automatic differentiation high (HSIL) versus low-grade (LSIL) intraepithelial lesions in HRA images...