2,294 research outputs found
Neuropathy Classification of Corneal Nerve Images Using Artificial Intelligence
Nerve variations in the human cornea have been associated with alterations in
the neuropathy state of a patient suffering from chronic diseases. For some diseases,
such as diabetes, detection of neuropathy prior to visible symptoms is important,
whereas for others, such as multiple sclerosis, early prediction of disease worsening is
crucial. As current methods fail to provide early diagnosis of neuropathy, in vivo
corneal confocal microscopy enables very early insight into the nerve damage by
illuminating and magnifying the human cornea. This non-invasive method captures a
sequence of images from the corneal sub-basal nerve plexus. Current practices of
manual nerve tracing and classification impede the advancement of medical research in
this domain. Since corneal nerve analysis for neuropathy is in its initial stages, there is
a dire need for process automation.
To address this limitation, we seek to automate the two stages of this process:
nerve segmentation and neuropathy classification of images. For nerve segmentation,
we compare the performance of two existing solutions on multiple datasets to select the
appropriate method and proceed to the classification stage. Consequently, we approach
neuropathy classification of the images through artificial intelligence using Adaptive
Neuro-Fuzzy Inference System, Support Vector Machines, NaĂŻve Bayes and k-nearest
neighbors. We further compare the performance of machine learning classifiers with
deep learning. We ascertained that nerve segmentation using convolutional neural networks provided a significant improvement in sensitivity and false negative rate by
at least 5% over the state-of-the-art software. For classification, ANFIS yielded the best
classification accuracy of 93.7% compared to other classifiers. Furthermore, for this
problem, machine learning approaches performed better in terms of classification
accuracy than deep learning
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Fuzzy logic: A âsimpleâ solution for complexities in neurosciences?
Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences
Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review
Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd
Machine Learning Techniques, Detection and Prediction of Glaucomaâ A Systematic Review
Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment
Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey
Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different abnormalities occurring within the optic disc region
Beyond imaging with coherent anti-Stokes Raman scattering microscopy
La microscopie optique permet de visualiser des Ă©chantillons biologiques avec une bonne sensibilitĂ© et une rĂ©solution spatiale Ă©levĂ©e tout en interfĂ©rant peu avec les Ă©chantillons. La microscopie par diffusion Raman cohĂ©rente (CARS) est une technique de microscopie non linĂ©aire basĂ©e sur lâeffet Raman qui a comme avantage de fournir un mĂ©canisme de contraste endogĂšne sensible aux vibrations molĂ©culaires. La microscopie CARS est maintenant une modalitĂ© dâimagerie reconnue, en particulier pour les expĂ©riences in vivo, car elle Ă©limine la nĂ©cessitĂ© dâutiliser des agents de contraste exogĂšnes, et donc les problĂšmes liĂ©s Ă leur distribution, spĂ©cificitĂ© et caractĂšre invasif. Cependant, il existe encore plusieurs obstacles Ă lâadoption Ă grande Ă©chelle de la microscopie CARS en biologie et en mĂ©decine : le coĂ»t et la complexitĂ© des systĂšmes actuels, les difficultĂ©s dâutilisation et dâentretient, la rigiditĂ© du mĂ©canisme de contraste, la vitesse de syntonisation limitĂ©e et le faible nombre de mĂ©thodes dâanalyse dâimage adaptĂ©es. Cette thĂšse de doctorat vise Ă aller au-delĂ de certaines des limites actuelles de lâimagerie CARS dans lâespoir que cela encourage son adoption par un public plus large. Tout dâabord, nous avons introduit un nouveau systĂšme dâimagerie spectrale CARS ayant une vitesse de syntonisation de longueur dâonde beaucoup plus rapide que les autres techniques similaires. Ce systĂšme est basĂ© sur un laser Ă fibre picoseconde synchronisĂ© qui est Ă la fois robuste et portable. Il peut accĂ©der Ă des lignes de vibration Raman sur une plage importante (2700â2950 cm-1) Ă des taux allant jusquâĂ 10 000 points spectrales par seconde. Il est parfaitement adaptĂ© pour lâacquisition dâimages spectrales dans les tissus Ă©pais. En second lieu, nous avons proposĂ© une nouvelle mĂ©thode dâanalyse dâimages pour lâĂ©valuation de la structure de la myĂ©line dans des images de sections longitudinales de moelle Ă©piniĂšre. Nous avons introduit un indicateur quantitatif sensible Ă lâorganisation de la myĂ©line et dĂ©montrĂ© comment il pourrait ĂȘtre utilisĂ© pour Ă©tudier certaines pathologies. Enfin, nous avons dĂ©veloppĂ© une mĂ©thode automatisĂ© pour la segmentation dâaxones myĂ©linisĂ©s dans des images CARS de coupes transversales de tissu nerveux. Cette mĂ©thode a Ă©tĂ© utilisĂ©e pour extraire des informations morphologique des fibres nerveuses dans des images CARS de grande Ă©chelle.Optical-based microscopy techniques can sample biological specimens using many contrast mechanisms providing good sensitivity and high spatial resolution while minimally interfering with the samples. Coherent anti-Stokes Raman scattering (CARS) microscopy is a nonlinear microscopy technique based on the Raman effect. It shares common characteristics of other optical microscopy modalities with the added benefit of providing an endogenous contrast mechanism sensitive to molecular vibrations. CARS is now recognized as a great imaging modality, especially for in vivo experiments since it eliminates the need for exogenous contrast agents, and hence problems related to the delivery, specificity, and invasiveness of those markers. However, there are still several obstacles preventing the wide-scale adoption of CARS in biology and medicine: cost and complexity of current systems as well as difficulty to operate and maintain them, lack of flexibility of the contrast mechanism, low tuning speed and finally, poor accessibility to adapted image analysis methods. This doctoral thesis strives to move beyond some of the current limitations of CARS imaging in the hope that it might encourage a wider adoption of CARS as a microscopy technique. First, we introduced a new CARS spectral imaging system with vibrational tuning speed many orders of magnitude faster than other narrowband techniques. The system presented in this original contribution is based on a synchronized picosecond fibre laser that is both robust and portable. It can access Raman lines over a significant portion of the highwavenumber region (2700â2950 cm-1) at rates of up to 10,000 spectral points per second and is perfectly suitable for the acquisition of CARS spectral images in thick tissue. Secondly, we proposed a new image analysis method for the assessment of myelin health in images of longitudinal sections of spinal cord. We introduced a metric sensitive to the organization/disorganization of the myelin structure and showed how it could be used to study pathologies such as multiple sclerosis. Finally, we have developped a fully automated segmentation method specifically designed for CARS images of transverse cross sections of nerve tissue.We used our method to extract nerve fibre morphology information from large scale CARS images
Fuzzy Logic in neurosurgery: Predicting poor outcomes after lumbar disk surgery in 501 consecutive patients
Background: Despite a lot of research into Patient selection, a significant number of Patients fail to benefit from surgery for symptomatic lumbar disk herniation. We have used Fuzzy Logic-based fuzzy inference system (FIS) for identifying Patients unlikely to improve after disk surgery and explored FIS as a tool for surgical outcome prediction.Methods: Data of 501 Patients were retrospectively reviewed for 54 independent variables. Sixteen variables were short-listed based on heuristics and were further classified into memberships with degrees of membership within each. A set of 11 rules was formed, and the rule base used individual membership degrees and their values mapped from the membership functions to perform Boolean Logical inference for a particular set of inputs. For each rule, a decision bar was generated that, when combined with the other rules in a similar way, constituted a decision surface. The FIS decisions were then based on calculating the centroid for the resulting decision surfaces and thresholding of actual centroid values. The results of FIS were then compared with eventual postoperative Patient outcomes based on clinical follow-ups at 6 months to evaluate FIS as a predictor of poor outcome.Results: Fuzzy inference system has a sensitivity of 88% and specificity of 86% in the prediction of Patients most likely to have poor outcome after lumbosacral miscrodiskectomy. The test thus has a positive predictive value of 0.36 and a negative predictive value of 0.98.Conclusion: Fuzzy inference system is a sensitive method of predicting Patients who will fail to improve with surgical intervention
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