Provided is a coated implantable medical device, comprising: a substrate; and a coating disposed on said substrate, wherein said coating comprises at least one polymer and at least one pharmaceutical agent in a therapeutically desirable morphology and/or at least one active biological agent and optionally, one or more pharmaceutical carrying agents; wherein substantially all of pharmaceutical agent and/or active biological agent remains within said coating and on said substrate until the implantable device is deployed at an intervention site inside the body of a subject and wherein upon deployment of said medical device in the body of said subject a portion of said pharmaceutical agent and/or active biological agent is delivered at said intervention site along with at least a portion of said polymer and/or a at least a portion of said pharmaceutical carrying agents.

Drug delivery medical device

Multi-feature based benchmark for cervical dysplasia classification evaluation

Cervical cancer causes the fourth most cancer-related deaths of women worldwide. Early detection of cervical intraepithelial neoplasia (CIN) can significantly increase the survival rate of patients. In this paper, we propose a deep learning framework for the accurate identification of LSIL+ (including CIN and cervical cancer) using time-lapsed colposcopic images. The proposed framework involves two main components, i.e., key-frame feature encoding networks and feature fusion network. The features of the original (pre-acetic-acid) image and the colposcopic images captured at around 60s, 90s, 120s and 150s during the acetic acid test are encoded by the feature encoding networks. Several fusion approaches are compared, all of which outperform the existing automated cervical cancer diagnosis systems using a single time slot. A graph convolutional network with edge features (E-GCN) is found to be the most suitable fusion approach in our study, due to its excellent explainability consistent with the clinical practice. A large-scale dataset, containing time-lapsed colposcopic images from 7,668 patients, is collected from the collaborative hospital to train and validate our deep learning framework. Colposcopists are invited to compete with our computer-aided diagnosis system. The proposed deep learning framework achieves a classification accuracy of 78.33%—comparable to that of an in-service colposcopist—which demonstrates its potential to provide assistance in the realistic clinical scenario.

Computer-Aided Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images

Endoscopic procedures form part of routine clinical practice for minimally invasive examinations and interventions. While they are beneficial for the patient, reducing surgical trauma and making convalescence times shorter, they make orientation and manipulation more challenging for the physician, due to the limited field of view through the endoscope. However, this drawback can be reduced by means of medical image processing and computer vision, using image stitching and surface reconstruction methods to expand the field of view. This paper provides a comprehensive overview of the current state of the art in endoscopic image stitching and surface reconstruction. The literature in the relevant fields of application and algorithmic approaches is surveyed. The technological maturity of the methods and current challenges and trends are analyzed.

Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

Goal: In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance. Methods: We developed algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol's iodine, and a combination of the two contrasts. Results: The proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively, when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, and 63% accuracy). Conclusion: The results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol's iodine images may provide unbiased automation of cervigrams. Significance: This would enable automated, expert-level diagnosis of cervical pre-cancer at the point of care.

/pdf/development-of-algorithms-for-automated-detection-of-j6b7smucvt.pdf

Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope

Automated image quality assessment methods, which include locating a region of interest, region assessment, contrast assessment, blur assessment, and contaminant detection, on video data and high-resolution imagery. Where the blur assessment is performed without a reference image by dividing the region into non-overlapping block, measuring the wavenumber frequency of the blocks and calculating the ratio of the low frequency to high frequency areas.

Method of image quality assessment to produce standardized imaging data

A process and device for detecting colon cancer by classifying and annotating clinical features in video data containing colonoscopic features by applying a probabilistic analysis to intra-frame and inter-frame relationships between colonoscopic features in spatially and temporally neighboring portions of video frames, and classifying and annotating as clinical features any of the colonoscopic features that satisfy the probabilistic analysis as clinical features. Preferably the probabilistic analysis is Hidden Markove Model analysis, and the process is carried out by a computer trained using semi supervised learning from labeled and unlabeled examples of clinical features in video containing colonoscopic features.

Versatile video interpretation, visualization, and management system

A method of automated image calibration that corrects for non-uniform illumination and calibrates color that is simple, fast, automated, accurate and reliable. A gray balance algorithm is applied to correct for non-uniform illumination and a color calibration algorithm is then applied to calibrate the human subject data. The system has been applied in multiple clinical sites with different instruments.

A method of automated image color calibration

Colposcopy is a primary diagnostic method used to detect cancer and precancerous lesions of the uterine cervix. During the examination, the metaplastic and abnormal tissues exhibit different degrees of whiteness (acetowhitening effect) after applying a 3%–5% acetic acid solution. Colposcopists evaluate the color and density of the acetowhite tissue to assess the severity of lesions for the purpose of diagnosis, telemedicine, and annotation. However, the color and illumination of the colposcopic images vary with the light sources, the instruments and camera settings, as well as the clinical environments. This makes assessment of the color information very challenging even for an expert. In terms of developing a Computer-Aided Diagnosis (CAD) system for colposcopy, these variations affect the performance of the feature extraction algorithm for the acetowhite color. Non-uniform illumination from the light source is also an obstacle for detecting acetowhite regions, lesion margins, and anatomic features. There fore, in digital colposcopy, it is critical to map the color appearance of the images taken with different colposcopes into one standard color space with normalized illumination. This paper presents a novel image calibration technique for colposcopic images. First, a specially designed calibration unit is mounted on the colposcope to acquire daily calibration data prior to performing subject examinations. The calibration routine is fast, automated, accurate and reliable. We then use our illumination correction algorithm and a color calibration algorithm to calibrate the exam data. In this paper we describe these techniques and demonstrate their applications in clinical studies.

A new image calibration system in digital colposcopy.

Automated image quality assessment algorithms, which perform the functions of locating a region of interest, maximizing the image contrast, and ensuring the region of interest is properly centered in the image Wherein the region of interest is located by spectral matching filter using a target spectrum obtained from samples of the image itself

A method to provide automated quality feedback to imaging devices to achieve standardized imaging data

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