Overview of technical solutions and assessment of clinical usefulness of capsule endoscopy

The paper presents an overview of endoscopic capsules with particular emphasis on technical aspects. It indicates common problems in capsule endoscopy such as: (1) limited wireless communication (2) the use of capsule endoscopy in the case of partial patency of the gastrointestinal tract, (3) limited imaging area, (4) external capsule control limitations. It also presents the prospects of capsule endoscopy, the most recent technical solutions for biopsy and the mobility of the capsule in the gastrointestinal tract. The paper shows the possibilities of increasing clinical usefulness of capsule endoscopy resulting from technological limitations. Attention has also been paid to the current role of capsule endoscopy in screening tests and the limitations of its effectiveness. The paper includes the author's recommendations concerning the direction of further research and the possibility of enhancing the scope of capsule endoscop

Blood pulsation measurement using cameras operating in visible light: limitations

Background: The paper presents an automatic method for analysis and processing of images from a camera operating in visible light. This analysis applies to images containing the human facial area (body) and enables to measure the blood pulse rate. Special attention was paid to the limitations of this measurement method taking into account the possibility of using consumer cameras in real conditions (different types of lighting, different camera resolution, camera movement). Methods: The proposed new method of image analysis and processing was associated with three stages: (1) image pre-processing-allowing for the image filtration and stabilization (object location tracking); (2) main image processing-allowing for segmentation of human skin areas, acquisition of brightness changes; (3) signal analysis-filtration, FFT (Fast Fourier Transformation) analysis, pulse calculation. Results and conclusions: The presented algorithm and method for measuring the pulse rate has the following advantages: (1) it allows for non-contact and non-invasive measurement; (2) it can be carried out using almost any camera, including webcams; (3) it enables to track the object on the stage, which allows for the measurement of the heart rate when the patient is moving; (4) for a minimum of 40,000 pixels, it provides a measurement error of less than ±2 beats per minute for p < 0.01 and sunlight, or a slightly larger error (±3 beats per minute) for artificial lighting; (5) analysis of a single image takes about 40 ms in Matlab Version 7.11.0.584 (R2010b) with Image Processing Toolbox Version 7.1 (R2010b)

Quantitative assessment of the impact of blood pulsation on intraocular pressure measurement results in healthy subjects

Background. Blood pulsation affects the results obtained using various medical devices in many different ways. Method. The paper proves the effect of blood pulsation on intraocular pressure measurements. Six measurements for each of the 10 healthy subjects were performed in various phases of blood pulsation. A total of 8400 corneal deformation images were recorded. The results of intraocular pressure measurements were related to the results of heartbeat phases measured with a pulse oximeter placed on the index finger of the subject's left hand. Results. The correlation between the heartbeat phase measured with a pulse oximeter and intraocular pressure is 0.69±0.26 (p<0.05). The phase shift calculated for the maximum correlation is equal to 60±40° (p<0.05). When the moment of measuring intraocular pressure with an air-puff tonometer is not synchronized, the changes in IOP for the analysed group of subjects can vary in the range of ±2.31 mmHg (p<0.3). Conclusions. Blood pulsation has a statistically significant effect on the results of intraocular pressure measurement. For this reason, in modern ophthalmic devices, the measurement should be synchronized with the heartbeat phases

Segmentation in dermatological hyperspectral images: dedicated methods

Background: Segmentation of hyperspectral medical images is one of many image segmentation methods which require profiling. This profiling involves either the adjustment of existing, known image segmentation methods or a proposal of new dedicated methods of hyperspectral image segmentation. Taking into consideration the size of analysed data, the time of analysis is of major importance. Therefore, the authors proposed three new dedicated methods of hyperspectral image segmentation with special reference to the time of analysis. Methods: The segmentation methods presented in this paper were tested and profiled to the images acquired from different hyperspectral cameras including SOC710 Hyperspectral Imaging System, Specim sCMOS-50-V10E. Correct functioning of the method was tested for over 10,000 2D images constituting the sequence of over 700 registrations of the areas of the left and right hand and the forearm. Results: As a result, three new methods of hyperspectral image segmentation have been proposed: fast analysis of emissivity curves (SKE), 3D segmentation (S3D) and hierarchical segmentation (SH). They have the following features: are fully automatic; allow for implementation of fast segmentation methods; are profiled to hyperspectral image segmentation; use emissivity curves in the model form, can be applied in any type of objects not necessarily biological ones, are faster (SKE-2.3 ms, S3D-1949 ms, SH-844 ms for the computer with Intel® Core i7 4960X CPU 3.6 GHz) and more accurate (SKE-accuracy 79 %, S3D-90 %, SH-92 %) in comparison with typical methods known from the literature. Conclusions: Profiling and/or proposing new methods of hyperspectral image segmentation is an indispensable element of developing software. This ensures speed, repeatability and low sensitivity of the algorithm to changing parameters

Machine learning and medicine: book review and commentary

This article is a review of the book “Master machine learning algorithms, discover how they work and implement them from scratch” (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery. com. An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book

Automatic method of analysis of OCT images in assessing the severity degree of glaucoma and the visual field loss

Introduction: In many practical aspects of ophthalmology, it is necessary to assess the severity degree of glaucoma in cases where, for various reasons, it is impossible to perform a visual field test - static perimetry. These are cases in which the visual field test result is not reliable, e.g. advanced AMD (Age-related Macular Degeneration). In these cases, there is a need to determine the severity of glaucoma, mainly on the basis of optic nerve head (ONH) and retinal nerve fibre layer (RNFL) structure. OCT is one of the diagnostic methods capable of analysing changes in both, ONH and RNFL in glaucoma. Material and method: OCT images of the eye fundus of 55 patients (110 eyes) were obtained from the SOCT Copernicus (Optopol Tech. SA, Zawiercie, Poland). The authors proposed a new method for automatic determination of the RNFL (retinal nerve fibre layer) and other parameters using: mathematical morphology and profiled segmentation based on morphometric information of the eye fundus. A quantitative ratio of the quality of the optic disk and RNFL – BGA (biomorphological glaucoma advancement) was also proposed. The obtained results were compared with the results obtained from a static perimeter. Results: Correlations between the known parameters of the optic disk as well as those suggested by the authors and the results obtained from static perimetry were calculated. The result of correlation with the static perimetry was 0.78 for the existing methods of image analysis and 0.86 for the proposed method. Practical usefulness of the proposed ratio BGA and the impact of the three most important features on the result were assessed. The following results of correlation for the three proposed classes were obtained: cup/disk diameter 0.84, disk diameter 0.97 and the RNFL 1.0. Thus, analysis of the supposed visual field result in the case of glaucoma is possible based only on OCT images of the eye fundus. Conclusions: The calculations and analyses performed with the proposed algorithm and BGA ratio confirm that it is possible to calculate supposed mean defect (MD) of the visual field test based on OCT images of the eye fundus

A clinical utility assessment of the automatic measurement method of the quality of Meibomian glands

No outside funding was received for this study., This research was supported by grants from the Medical University of Silesia in Katowice, Grant No. KNW-1-023/N/6/0; the National Natural Science Foundation of China (31600758); Beijing Natural Science Foundation (7174287); the priming scientific research foundation for the junior researcher in Beijing Tongren Hospital, Capital Medical University (2015-YJJZZL- 008) and Beijing Key Laboratory of Ophthalmology and Visual Science (2016YKSJ02).Background: Meibomian gland dysfunction (MGD) is one of the most common diseases observed in clinics and is the leading cause of evaporative dry eye. Today, diagnostics of MGD is not fully automatic yet and is based on a qualitative assessment made by an ophthalmologist. Therefore, an automatic analysis method was developed to assess MGD quantiatively. Materials: The analysis made use of 228 images of 57 patients recorded by OCULUS Keratograph® 5 M with a resolution of 1024 × 1360 pixels concern 30 eyes of healthy individuals (14 women and 16 men) and 27 eyes of sick patients (10 women and 17 men). The diagnosis of dry eye was made according to the consensus of DED in China (2013). Methods: The presented method of analysis is a new, developed method enabling an automatic, reproducible and quantitative assessment of Meibomian glands. The analysis relates to employing the methods of analysis and image processing. The analysis was conducted in the Matlab environment Version 7.11.0.584, R2010b, Java VM Version: Java 1.6.0_17-b04 with Sun Microsystems Inc. with toolboxes: Statistical, Signal Processing and Image Processing. Results: The presented, new method of analysis of Meibomian glands is fully automatic, does not require operator's intervention, allows obtaining reproducible results and enables a quantitative assessment of Meibomian glands. Compared to the other known methods, particularly with the method described in literature it allows obtaining better sensitivity (98%) and specificity (100%) results by 2%.Medical University of Silesia in Katowice; National Natural Science Foundation of China; Natural Science Foundation of Beijing Municipalit

Simplified automatic method for measuring the visual field using the perimeter ZERK 1

Background: Currently available perimeters have limited capabilities of performing measurements of the visual field in children. In addition, they do not allow for fully automatic measurement even in adults. The patient in each case (in any type of perimeter) has at his disposal a button which he uses to indicate that he has seen a light stimulus. Such restrictions have been offset in the presented new perimeter ZERK 1. Methods: The paper describes a new type of automated, computerized perimeter designed to test the visual field in children and adults. The new perimeter and proprietary software enable to carry out tests automatically (without the need to press any button). The presented full version of the perimeter has been tested on a head phantom. The next steps will involve clinical trials and a comparison with measurements obtained using other types of perimeters. Results: The perimeter ZERK 1 enables automatic measurement of the visual field in two axes (with a span of 870 mm and a depth of 525 mm) with an accuracy of not less than 1o (95 LEDs on each arm) at a typical position of the patient's head. The measurement can be carried out in two modes: default/typical (lasting about 1 min), and accurate (lasting about 10 min). Compared with available and known types of perimeters, it has an open canopy, proprietary software and cameras tracking the eye movement, automatic control of fixation points, light stimuli with automatically preset light stimulus intensity in the following ranges: 550-700 mcd (red 620-630 nm), 1100-1400 mcd (green 515-530 nm), 200-400 mcd (blue 465-475 nm). Conclusions: The paper presents a new approach to the construction of perimeters based on automatic tracking of the eye movements in response to stimuli. The unique construction of the perimeter and the software allow for its mobile use in the examination of children and bedridden patients

Corneal power evaluation after myopic corneal refractive surgery using artificial neural networks

Background: Efficacy and high availability of surgery techniques for refractive defect correction increase the number of patients who undergo to this type of surgery. Regardless of that, with increasing age, more and more patients must undergo cataract surgery. Accurate evaluation of corneal power is an extremely important element affecting the precision of intraocular lens (IOL) power calculation and errors in this procedure could affect quality of life of patients and satisfaction with the service provided. The available device able to measure corneal power have been tested to be not reliable after myopic refractive surgery. Methods: Artificial neural networks with error backpropagation and one hidden layer were proposed for corneal power prediction. The article analysed the features acquired from the Pentacam HR tomograph, which was necessary to measure the corneal power. Additionally, several billion iterations of artificial neural networks were conducted for several hundred simulations of different network configurations and different features derived from the Pentacam HR. The analysis was performed on a PC with Intel® Xeon® X5680 3.33 GHz CPU in Matlab® Version 7.11.0.584 (R2010b) with Signal Processing Toolbox Version 7.1 (R2010b), Neural Network Toolbox 7.0 (R2010b) and Statistics Toolbox (R2010b). Results and conclusions: A total corneal power prediction error was obtained for 172 patients (113 patients forming the training set and 59 patients in the test set) with an average age of 32 ± 9.4 years, including 67% of men. The error was at an average level of 0.16 ± 0.14 diopters and its maximum value did not exceed 0.75 dioptres. The Pentacam parameters (measurement results) providing the above result are tangential anterial/posterior. The corneal net power and equivalent k-reading power. The analysis time for a single patient (a single eye) did not exceed 0.1 s, whereas the time of network training was about 3 s for 1000 iterations (the number of neurons in the hidden layer was 400