scholarly journals Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence

2021 ◽  
Vol 11 (8) ◽  
pp. 803
Author(s):  
Eva M. Sánchez-Morla ◽  
Juan L. Fuentes ◽  
Juan M. Miguel-Jiménez ◽  
Luciano Boquete ◽  
Miguel Ortiz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bipolar Disorder ◽  
Optical Coherence Tomography ◽  
Feature Vector ◽  
High Capacity ◽  
Area Under The Curve ◽  
Support Vector ◽  
Type I ◽  
Optical Coherence ◽  
K Nearest Neighbors

Background: The aim of this study is to explore an objective approach that aids the diagnosis of bipolar disorder (BD), based on optical coherence tomography (OCT) data which are analyzed using artificial intelligence. Methods: Structural analyses of nine layers of the retina were analyzed in 17 type I BD patients and 42 controls, according to the areas defined by the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The most discriminating variables made up the feature vector of several automatic classifiers: Gaussian Naive Bayes, K-nearest neighbors and support vector machines. Results: BD patients presented retinal thinning affecting most layers, compared to controls. The retinal thickness of the parafoveolar area showed a high capacity to discriminate BD subjects from healthy individuals, specifically for the ganglion cell (area under the curve (AUC) = 0.82) and internal plexiform (AUC = 0.83) layers. The best classifier showed an accuracy of 0.95 for classifying BD versus controls, using as variables of the feature vector the IPL (inner nasal region) and the INL (outer nasal and inner inferior regions) thickness. Conclusions: Our patients with BD present structural alterations in the retina, and artificial intelligence seem to be a useful tool in BD diagnosis, but larger studies are needed to confirm our findings.

scholarly journals Application of Artificial Intelligence-based Dual-modality Analysis Combining Fundus Photography and Optical Coherence Tomography in Diabetic Retinopathy Screening in a Community Hospital

2021 ◽  
Author(s):  
Rui Liu ◽  
Qingchen Li ◽  
Feiping Xu ◽  
Shasha Wang ◽  
Jie He ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Community Hospital ◽  
Area Under The Curve ◽  
Fundus Photography ◽  
Diabetic Patients ◽  
Optical Coherence ◽  
Sensitivity Specificity

Abstract Background To assess the feasibility and application value of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) by combining fundus photos and optical coherence tomography (OCT) images in a community hospital.Methods Fundus photos and OCT images were taken for 600 diabetic patients in a community hospital. Ophthalmologists and AI identified these fundus photos according to international DR standards. OCT images were used to detect concomitant macular oedema (ME). The criteria for referral were DR grades 2-4 and/or the presence of ME. The sensitivity and specificity of AI grading were evaluated. The number of referable DR cases confirmed by ophthalmologists and AI was calculated.Results DR was detected in 81 (13.5%) participants by ophthalmologists and in 94 (15.6%) by AI, and 45 (7.5%) and 53 (8.8%) participants were diagnosed with referable DR by ophthalmologists and by AI, respectively. The sensitivity, specificity and area under the curve (AUC) of AI for detecting DR were 91.67%, 96.92% and 0.944, respectively. For detecting referable DR, the sensitivity, specificity and AUC of AI were 97.78%, 98.38% and 0.981, respectively. ME was detected from OCT images in 49 (8.2%) participants by ophthalmologists and in 57 (9.5%) by AI, and the sensitivity, specificity and AUC of AI were 91.30%, 97.46% and 0.944, respectively. When combining fundus photos and OCT images, the number of referrals identified by ophthalmologists increased from 45 to 75 and from 53 to 85 by AI.Conclusion AI-based DR screening has high sensitivity and specificity and may feasibly improve the referral rate of community DR.

scholarly journals Errata: Advantages of microscope-integrated intraoperative online optical coherence tomography: usage in Boston keratoprosthesis type I surgery

2016 ◽  
Vol 21 (2) ◽  
pp. 029801 ◽  
Author(s):  
Sebastian Siebelmann ◽  
Philipp Steven ◽  
Deniz Hos ◽  
Gereon Hüttmann ◽  
Eva Lankenau ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Type I ◽  
Optical Coherence ◽  
Boston Keratoprosthesis

Three-Dimensional Analysis of Enamel Crack Behavior Using Optical Coherence Tomography

2016 ◽  
Vol 96 (3) ◽  
pp. 308-314 ◽  
Author(s):  
M.S. Segarra ◽  
Y. Shimada ◽  
A. Sadr ◽  
Y. Sumi ◽  
J. Tagami
Keyword(s):  
Optical Coherence Tomography ◽  
Crack Pattern ◽  
Type I ◽  
Optical Coherence ◽  
Type Ii ◽  
Human Teeth ◽  
Posterior Teeth ◽  
Crack Patterns ◽  
Type Iii ◽  
Crack Behavior

The aim of this study was to nondestructively analyze enamel crack behavior on different areas of teeth using 3D swept source-optical coherence tomography (SS-OCT). Ten freshly extracted human teeth of each type on each arch ( n = 80 teeth) were inspected for enamel crack patterns on functional, contact and nonfunctional, or noncontact areas using 3D SS-OCT. The predominant crack pattern for each location on each specimen was noted and analyzed. The OCT observations were validated by direct observations of sectioned specimens under confocal laser scanning microscopy (CLSM). Cracks appeared as bright lines with SS-OCT, with 3 crack patterns identified: Type I – superficial horizontal cracks; Type II – vertically (occluso-gingival) oriented cracks; and Type III – hybrid or complicated cracks, a combination of a Type I and Type III cracks, which may or may not be confluent with each other. Type II cracks were predominant on noncontacting surfaces of incisors and canines and nonfunctional cusps of posterior teeth. Type I and III cracks were predominant on the contacting surfaces of incisors, cusps of canines, and functional cusps of posterior teeth. Cracks originating from the dental-enamel junction and enamel tufts, crack deflections, and the initiation of new cracks within the enamel (internal cracks) were observed as bright areas. CLSM observations corroborated the SS-OCT findings. We found that crack pattern, tooth type, and the location of the crack on the tooth exhibited a strong correlation. We show that the use of 3D SS-OCT permits for the nondestructive 3D imaging and analysis of enamel crack behavior in whole human teeth in vitro. 3D SS-OCT possesses potential for use in clinical studies for the analysis of enamel crack behavior.

scholarly journals A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2444 ◽  
Author(s):  
Dieu Tien Bui ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Kamran Chapi ◽  
Ebrahim Omidavr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Kernel Functions ◽  
Gully Erosion ◽  
Support Vector ◽  
Base Function ◽  
Conditioning Factors ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study

2018 ◽  
Vol 102 (11) ◽  
pp. 1564-1569 ◽  
Author(s):  
Harpal Singh Sandhu ◽  
Nabila Eladawi ◽  
Mohammed Elmogy ◽  
Robert Keynton ◽  
Omar Helmy ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Optical Coherence Tomography Angiography ◽  
Area Under The Curve ◽  
Foveal Avascular Zone ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Cross Sectional ◽  
Cad System

BackgroundOptical coherence tomography angiography (OCTA) is increasingly being used to evaluate diabetic retinopathy, but the interpretation of OCTA remains largely subjective. The purpose of this study was to design a computer-aided diagnostic (CAD) system to diagnose non-proliferative diabetic retinopathy (NPDR) in an automated fashion using OCTA images.MethodsThis was a two-centre, cross-sectional study. Adults with type II diabetes mellitus (DMII) were eligible for inclusion. OCTA scans of the macula were taken, and the five vascular maps generated per eye were analysed by a novel CAD system. For the purpose of classification/diagnosis, three different local features—blood vessel density, blood vessel calibre and the size of the foveal avascular zone (FAZ)—were segmented from these images and used to train a new, automated classifier.ResultsOne hundred and six patients with DMII were included in the study, 23 with no DR and 83 with mild NPDR. When using features of the superficial retinal map alone, the system demonstrated an accuracy of 80.0% and area under the curve (AUC) of 76.2%. Using the features of the deep retinal map alone, accuracy was 91.4% and AUC 89.2%. When data from both maps were combined, the presented CAD system demonstrated overall accuracy of 94.3%, sensitivity of 97.9%, specificity of 87.0%, area under curve (AUC) of 92.4% and dice similarity coefficient of 95.8%.ConclusionAutomated diagnosis of NPDR using OCTA images is feasible and accurate. Combining this system with OCT data is a plausible next step that would likely improve its robustness.

scholarly journals Fundus Fluorescein Angiography and Optical Coherence Tomography Angiography Findings in Diabetic Retinopathy and Diabetic Macular Edema

2018 ◽  
pp. 130-137
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Fluorescein Angiography ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Optical Coherence Tomography Angiography ◽  
Imaging Technique ◽  
Diabetic Patients ◽  
Type I ◽  
Optical Coherence

Diabetic retinopathy is an important public health issue as its prevalence has been increasing every year. It is one of the major causes of visual loss which can be preventable with early diagnosis and appropriate treatment. The fundus examination must be done in detail using mydriatics, and digital images must be recorded in all diabetic patients with special emphasis on the disease type (type I and type II), duration, and prognosis. Fluorescein angiography (FA) is a gold standard invasive retinal imaging technique for the diagnosis, monitoring, and evaluating the response of the treatment in diabetic patients, but FA has limitations due to possible side effects. Optical coherence tomography angiography (OCTA) is a recent, non-invasive, dye-free imaging technique that can be used in every visit. It has the capability to image all retinal and choroidal vascular layers (segmentation) and quantify macular ischemia in a short period of time which is beneficial for the patient, and the ophthalmologist. The aim of this review is to address the findings, advantages, and disadvantages of FA and OCTA in patients with diabetic retinopathy and diabetic macular edema.

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