Development of adhesion test for coated medical device

High biocompatibility is a basic requirement in medical technology. Polymer coatings can radically improve medical device biocompatibility, especially for surfaces like stainless steel. Adhesion is an important quality in a coating, and this was our rationale for developing a polymer adhesion testing protocol. We compared two biocompatible polymers, polyurethane (PUR) and poly-(DL-lactic-co-glycolic acid) (PDLG). Po lymer layers were created on surface-treated stainless steel. The properties of different la yers were compared. Adhesion of the coatings was characterised by concentration of coating so lution, rate of the contacted surface and surface roughness of the carriers. PUR showed better adhesion under our test conditions

Anodisation of medical grade titanium

In recent years the number of titanium dental implants in use has significantly increased. At the same time bacterial infection of implants has become more common. The goal of our study was to develop a titanium-dioxide layer on the surface of titanium implant materials by anodisation with a view to impeding the attachment of contagious bacteria. In our experiments Grade 2 titanium and nanograin Grade 2 titanium discs were subjected to anodisation. We investigated the effect of voltage on the surface pattern of emerging titanium-dioxide. We examined the surfaces by reflected-light microscopy. We found that the value of the applied voltage and variation in grain size affected the thickness of the formed titanium-dioxide layer. These layers may promote or support desired forms of biological activity, such as cell attachment to integrate with bone

Chemical etching of titanium samples

We studied chemical etching treatment on the surface of titanium implant materials, specifically 2 mm thick Grade 2 and nanoparticulate titanium discs, aiming to modify the surface roughness of samples. For chemical etching we investigated changes in reaction time on the surface (15-600 seconds). During the research we obtained the changes of thickness, mass and the surface roughness on both sides of every disc after the acid etching. The resulting surface was examined with optical stereo and reflected-light microscopy and electron microscopy. As a result we found that the optimal etching parameters are an etching time of 30 seconds, etching solution of 9 V/V% hydrofluoric acid, 12 V/V% nitric acid and distilled water and a temperature of 30°C, because with this protocol the burr from milling detaches from the surface

Investigation of metallic surface area of coronary stents

OBJECTIVES: Endovascular stents, such as coronary stents, are widely used for the treatment of narrowed or blocked blood vessels caused by plaque formation in the arteries. The narrowing of expanded blood vessels (restenosis) is perhaps the major complication associated with endovascular stent implantation that is believed to be caused by insufficient metallic surface area (MSA) in some stent designs. Our aim was to compare three examination methods which were developed at our department, to measure stent surface areas. METHODS: The first method was manually performed using rotating equipment under a stereomicroscope. The second method, which has recently been developed, is an automated method using an integrated scanner and a rotating engine. Both methods aimed at converting the cylindrical stent into a flattened two-dimensional image in order to enable the measurement of stent surface area by imaging software. The third method is based on a calculation which uses various stent values such as diameter, length, and strut thickness. Each measurement process was tested on different types of stents. RESULTS: Our findings showed that the methods gave similar results. The largest differences between the methods were speed and accuracy. CONCLUSIONS: The results lead us to propose favouring the automated rotation method

Investigation of the efficiency of an interconnected convolutional neural network by classifying medical images

Convolutional Neural Network (CNN) for medical image classification has produced satisfying work [11, 12, 15]. Several pretrained models such as VGG19 [17], InceptionV3 [18], and MobileNet [8] are architectures that can be relied on to design high accuracy classification models. This work investigates the performance of three pretrained models with two methods of training. The first method trains the model independently, meaning that each model is given an input and trained separately, then the best results are determined by majority voting. In the second method the three pretrained models are trained simultaneously as interconnected models. The interconnected model adopts an ensemble architecture as is shown in [7]. By training multiple CNNs, this work gives optimum results compared to a single CNN. The difference is that the three subnetworks are trained simultaneously in an interconnected network and showing one expected result. In the training process the interconnected model determines each subnetwork’s weight by itself. Furthermore, this model will apply the most suitable weight to the final decision. The interconnected model showed comparable performance after training on several datasets. The measurement includes comparing the Accuracy, Precision and Recall scores as is shown in confusion matrix [3, 14]

Anodisation of medical grade titanium

In recent years the number of titanium dental implants in use has significantly increased. At the same time bacterial infection of implants has become more common. The goal of our study was to develop a titanium-dioxide layer on the surface of titanium implant materials by anodisation with a view to impeding the attachment of contagious bacteria. In our experiments Grade 2 titanium and nanograin Grade 2 titanium discs were subjected to anodisation. We investigated the effect of voltage on the surface pattern of emerging titanium-dioxide. We examined the surfaces by reflected-light microscopy. We found that the value of the applied voltage and variation in grain size affected the thickness of the formed titanium-dioxide layer. These layers may promote or support desired forms of biological activity, such as cell attachment to integrate with bone.  DOI: 10.17489/biohun/2013/1/2

Development of adhesion test for coated medical device

High biocompatibility is a basic requirement in medical technology. Polymer coatings can radically improve medical device biocompatibility, especially for surfaces like stainless steel. Adhesion is an important quality in a coating, and this was our rationale for developing a polymer adhesion testing protocol. We compared two biocompatible polymers, polyurethane (PUR) and poly-(DL-lactic-co-glycolic acid) (PDLG). Polymer layers were created on surface-treated stainless steel. The properties of different layers were compared. Adhesion of the coatings was characterised by concentration of coating solution, rate of the contacted surface and surface roughness of the carriers. PUR showed better adhesion under our test conditions.  DOI: 10.17489/biohun/2013/1/3

Comparison of single and ensemble-based convolutional neural networks for cancerous image classification

In this work, we investigated the ability of several Convolutional Neural Network (CNN) models for predicting the spread of cancer using medical images. We used a dataset released by the Kaggle, namely PatchCamelyon. The dataset consists of 220,025 pathology images digitized by a tissue scanner. A clinical expert labeled each image as cancerous or non-cancerous. We used 70% of the images as a training set and 30% of them as a validation set. We design three models based on three commonly used modules: VGG, Inception, and Residual Network (ResNet), to develop an ensemble model and implement a voting system to determine the final decision. Then, we compared the performance of this ensemble model to the performance of each single model. Additionally, we used a weighted majority voting system, where the final prediction is equal to the weighted average of the prediction produced by each network. Our results show that the classification of the two ensemble models reaches 96%. Thus these results prove that the ensemble model outperforms single network architectures