Deep learning segmentation architectures for automatic detection of pancreatic ductal adenocarcinoma in EUS-guided fine-needle biopsy samples based on whole-slide imaging.

Background: EUS-guided fine-needle biopsy is the procedure of choice for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). Nevertheless, the samples obtained are small and require expertise in pathology, whereas the diagnosis is difficult in view of the scarcity of malignant cells and the important desmoplastic reaction of these tumors. With the help of artificial intelligence, the deep learning architectures produce a fast, accurate, and automated approach for PDAC image segmentation based on whole-slide imaging.

Experimental and theoretical model of microvascular network remodeling and blood flow redistribution following minimally invasive microvessel laser ablation.

Overly dense microvascular networks are treated by selective reduction of vascular elements. Inappropriate manipulation of microvessels could result in loss of host tissue function or a worsening of the clinical problem. Here, experimental, and computational models were developed to induce blood flow changes via selective artery and vein laser ablation and study the compensatory collateral flow redistribution and vessel diameter remodeling.

Accuracy of instrument tip position using fiber optic shape sensing for navigated bronchoscopy.

The purpose of this study was to evaluate the accuracy of a method for estimating the tip position of a fiber optic shape-sensing (FOSS) integrated instrument being inserted through a bronchoscope. A modified guidewire with a multicore optical fiber was inserted into the working channel of a custom-made catheter with three electromagnetic (EM) sensors. The displacement between the instruments was manually set, and a point-based method was applied to match the position of the EM sensors to corresponding points on the shape.

Mechanical and Computational Fluid Dynamic Models for Magnesium-Based Implants.

Today, mechanical properties and fluid flow dynamic analysis are considered to be two of the most important steps in implant design for bone tissue engineering. The mechanical behavior is characterized by Young's modulus, which must have a value close to that of the human bone, while from the fluid dynamics point of view, the implant permeability and wall shear stress are two parameters directly linked to cell growth, adhesion, and proliferation. In this study, we proposed two simple geometries with a three-dimensional pore network dedicated to a manufacturing route based on a titanium wire waving procedure used as an intermediary step for Mg-based implant fabrication.

Experimental and Theoretical Model of Single Vessel Minimally Invasive Micro-Laser Ablation: Inducing Microvascular Network Remodeling and Blood Flow Redistribution Without Compromising Host Tissue Function.

Overly dense microvascular networks are treated by selective reduction of vascular elements. Inappropriate manipulation of microvessels could result in loss of host tissue function or a worsening of the clinical problem. Here, experimental, and computational models were developed to induce blood flow changes via selective artery and vein laser ablation and study the compensatory collateral flow redistribution and vessel diameter remodeling.

Bone Regeneration Induced by Patient-Adapted Mg Alloy-Based Scaffolds for Bone Defects: Present and Future Perspectives.

Treatment of bone defects resulting after tumor surgeries, accidents, or non-unions is an actual problem linked to morbidity and the necessity of a second surgery and often requires a critical healthcare cost. Although the surgical technique has changed in a modern way, the treatment outcome is still influenced by patient age, localization of the bone defect, associated comorbidities, the surgeon approach, and systemic disorders. Three-dimensional magnesium-based scaffolds are considered an important step because they can have precise bone defect geometry, high porosity grade, anatomical pore shape, and mechanical properties close to the human bone.

Feasibility of a lung airway navigation system using fiber-Bragg shape sensing and artificial intelligence for early diagnosis of lung cancer.

Currently early diagnosis of malignant lesions at the periphery of lung parenchyma requires guidance of the biopsy needle catheter from the bronchoscope into the smaller peripheral airways via harmful X-ray radiation. Previously, we developed an image-guided system, iMTECH which uses electromagnetic tracking and although it increases the precision of biopsy collection and minimizes the use of harmful X-ray radiation during the interventional procedures, it only traces the tip of the biopsy catheter leaving the remaining catheter untraceable in real time and therefore increasing image registration error. To address this issue, we developed a shape sensing guidance system containing a fiber-Bragg grating (FBG) catheter and an artificial intelligence (AI) software, AIrShape to track and guide the entire biopsy instrument inside the lung airways, without radiation or electromagnetic navigation.

Assessment of Deep Learning Methods for Differentiating Autoimmune Disorders in Ultrasound Images.

At present, deep learning becomes an important tool in medical image analysis, with good performance in diagnosing, pattern detection, and segmentation. Ultrasound imaging offers an easy and rapid method to detect and diagnose thyroid disorders. With the help of a computer-aided diagnosis (CAD) system based on deep learning, we have the possibility of real-time and non-invasive diagnosing of thyroidal US images.

Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model.

Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e.

Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods.

: At present, thyroid disorders have a great incidence in the worldwide population, so the development of alternative methods for improving the diagnosis process is necessary. : For this purpose, we developed an ensemble method that fused two deep learning models, one based on convolutional neural network and the other based on transfer learning. For the first model, called 5-CNN, we developed an efficient end-to-end trained model with five convolutional layers, while for the second model, the pre-trained VGG-19 architecture was repurposed, optimized and trained.

Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images.

Aim: In this paper we proposed different architectures of convolutional neural network (CNN) to classify fatty liver disease in images using only pixels and diagnosis labels as input. We trained and validated our models using a dataset of 629 images consisting of 2 types of liver images, normal and liver steatosis.

Material And Methods: We assessed two pre-trained models of convolutional neural networks, Inception-v3 and VGG-16 using fine-tuning.

Finite Element Analysis of a Novel Aortic Valve Stent.

Worldwide, one of the leading causes of death for patients with cardiovascular disease is aortic valve failure or insufficiency as a result of calcification and cardiovascular disease. The surgical treatment consists of repair or total replacement of the aortic valve. Artificial aortic valve implantation via a percutaneous or endovascular procedure is the minimally invasive alternative to open chest surgery, and the only option for high-risk or older patients.

Skin Diseases Classification Using Deep Leaning Methods.

Due to the high incidence of skin tumors, the development of computer aided-diagnosis methods will become a very powerful diagnosis tool for dermatologists. The skin diseases are initially diagnosed visually, through clinical screening and followed in some cases by dermoscopic analysis, biopsy and histopathological examination. Automatic classification of dermatoscopic images is a challenge due to fine-grained variations in lesions.

Preoperative Computer-Assisted Laparoscopy Planning for the Minimally Invasive Surgical Repair of Hiatal Hernia.

Minimal invasive surgical procedures such as laparoscopy are preferred over open surgery due to faster postoperative recovery, less trauma and inflammatory response, and less scarring. Laparoscopic repairs of hiatal hernias require pre-procedure planning to ensure appropriate exposure and positioning of the surgical ports for triangulation, ergonomics, instrument length and operational angles to avoid the fulcrum effect of the long and rigid instruments. We developed a novel surgical planning and navigation software, iMTECH to determine the optimal location of the skin incision and surgical instrument placement depth and angles during laparoscopic surgery.

Feasibility of a Portable Abdominal Insufflation Device for Controlling Intraperitoneal Bleeding After Abdominal Blunt Trauma.

Uncontrolled bleeding contributes to 30% to 40% of trauma-related deaths and is the leading cause of potentially preventable deaths. Currently, there is no effective method available to first responders for temporary control of noncompressible intraabdominal bleeding while patients are transported to the hospital. Our previous studies demonstrated that abdominal insufflation provides effective temporary bleeding control.

Intraoperative localized constrained registration in navigated bronchoscopy.

Purpose: One of the major challenges in electromagnetic navigated bronchoscopy is the navigation accuracy. An initial rigid image-to-patient registration may not be optimal for the entire lung volume, as the lung tissue anatomy is likely to have shifted since the time of computer tomography (CT) acquisition. The accuracy of the initial rigid registration will also be affected throughout the procedure by breathing, coughing, patient movement and tissue displacements due to pressure from bronchoscopy tools.

Utility of confocal laser endomicroscopy in pulmonology and lung cancer.

Primary lung cancer is an increasing health issue worldwide, with an ever-growing incidence due to various risk factors dispersed in all settings of modern society. Late discovery and poor survival rates for patients that do not qualify for surgical treatments greatly decrease overall mortality. Imaging methods remain powerful tools for early detection; however, molecular profiling is currently required for better understanding treatment options and for developing novel agents.

Computer Aided Diagnosis for Confocal Laser Endomicroscopy in Advanced Colorectal Adenocarcinoma.

Introduction: Confocal laser endomicroscopy (CLE) is becoming a popular method for optical biopsy of digestive mucosa for both diagnostic and therapeutic procedures. Computer aided diagnosis of CLE images, using image processing and fractal analysis can be used to quantify the histological structures in the CLE generated images. The aim of this study is to develop an automatic diagnosis algorithm of colorectal cancer (CRC), based on fractal analysis and neural network modeling of the CLE-generated colon mucosa images.

Implantable tissue isolation chambers for analyzing tumor dynamics in vivo.

Recruitment of new blood vessels from the surrounding tissue is central to tumor progression and involves a fundamental transition of the normal, organized vasculature into a dense disarray of vessels that infiltrates the tumor. At present, studying the co-development of the tumor and recruited normal tissue is experimentally challenging because many of the important events occur rapidly and over short length scales in a dense three-dimensional space. To overcome these experimental limitations, we partially confined tumors within biocompatible and optically clear tissue isolation chambers (TICs) and implanted them in mice to create a system that is more amenable to microscopic analysis.

A novel fusion imaging system for endoscopic ultrasound.

Background And Objective: Navigation of a flexible endoscopic ultrasound (EUS) probe inside the gastrointestinal (GI) tract is problematic due to the small window size and complex anatomy. The goal of the present study was to test the feasibility of a novel fusion imaging (FI) system which uses electromagnetic (EM) sensors to co-register the live EUS images with the pre-procedure computed tomography (CT) data with a novel navigation algorithm and catheter.

Methods: An experienced gastroenterologist and a novice EUS operator tested the FI system on a GI tract bench top model.

Tumor neoangiogenesis detection by confocal laser endomicroscopy and anti-CD105 antibody: Pilot study.

Aim: To evaluate neoangiogenesis in patients with colon cancer by two fluorescently labeled antibodies on fresh biopsy samples imaged with confocal laser endomicroscopy (CLE).

Methods: CLE is an imaging technique for gastrointestinal endoscopy providing in vivo microscopy at subcellular resolution. An important question in validating tumor angiogenesis is what proportion of the tumor vascular network is represented by pre-existing parent tissue vessels and newly formed vessels.

Periprosthetic bone remodelling in total knee arthroplasty.

Introduction: The clinical studies have shown that the displacement of the prosthesis components, especially of the tibial one is higher during the first year, after which it reaches an equilibrum position compatible with a good long term functioning. This displacement takes place due to bone remodelling close to the implant secondary to different loading concentrations over different areas of bone.

Material And Method: Our study implies a simulation on a computational model using the finite element analysis.

Evaluation of new morphometric parameters of neoangiogenesis in human colorectal cancer using confocal laser endomicroscopy (CLE) and targeted panendothelial markers.

The tumor microcirculation is characterized by an abnormal vascular network with dilated, tortuous and saccular vessels. Therefore, imaging the tumor vasculature and determining its morphometric characteristics represent a critical goal for optimizing the cancer treatment that targets the blood vessels (i.e.