Automated Keratoconus Detection by 3D Corneal Images Reconstruction

This paper presents a technique for the detection of keratoconus via the construction of a 3D eye images from 2D frontal and lateral eye images. Keratoconus is a disease that affects the cornea. Normal case eyes have a round-shaped cornea, while patients who suffer from keratoconus have a cone-shaped cornea. Early diagnosis can decrease the risk of eyesight loss. Our aim is to create a method of fully automated keratoconus detection using digital-camera frontal and lateral eye images. The presented technique accurately determines case severity. Geometric features are extracted from 2D images to estimate depth information used to build 3D images of the cornea. The proposed methodology is easy to implement and time-efficient. 2D images of the eyes (frontal and lateral) are used as input, and 3D images from which the curvature of the cornea can be detected are produced as output. Our method involves two main steps: feature extraction and depth calculation. Machine learning from the 3D images dataset Dataverse, specifically taken by the Cornea/Anterior Segment OCT SS-1000 (CASIA), was performed. Results show that the method diagnosed the four stages of keratoconus (severe, moderate, mild, and normal) with an accuracy of 97.8%, as compared to manual diagnosis done by medical experts.

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93 A machine learning approach to classification of uveitis in children using anterior segment OCT images of the eye

10.1136/archdischild-2019-gosh.93 ◽

2019 ◽

Author(s):

Usmaan Bhatti ◽

Saira Akbarali ◽

Ameenat L Solebo ◽

William Bryant

Keyword(s):

Machine Learning ◽

Anterior Segment ◽

Learning Approach ◽

Anterior Segment Oct ◽

Machine Learning Approach

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Do 3D Face Images Capture Cues of Strength, Weight, and Height Better than 2D Face Images do?

Adaptive Human Behavior and Physiology ◽

10.1007/s40750-021-00170-8 ◽

2021 ◽

Vol 7 (3) ◽

pp. 209-219

Author(s):

Iris J Holzleitner ◽

Alex L Jones ◽

Kieran J O’Shea ◽

Rachel Cassar ◽

Vanessa Fasolt ◽

...

Keyword(s):

Three Dimensional ◽

Physical Characteristics ◽

Two Dimensional ◽

3D Images ◽

3D Face ◽

Face Images ◽

Similar Accuracy ◽

2D And 3D ◽

2D Images ◽

Better Than

Abstract Objectives A large literature exists investigating the extent to which physical characteristics (e.g., strength, weight, and height) can be accurately assessed from face images. While most of these studies have employed two-dimensional (2D) face images as stimuli, some recent studies have used three-dimensional (3D) face images because they may contain cues not visible in 2D face images. As equipment required for 3D face images is considerably more expensive than that required for 2D face images, we here investigated how perceptual ratings of physical characteristics from 2D and 3D face images compare. Methods We tested whether 3D face images capture cues of strength, weight, and height better than 2D face images do by directly comparing the accuracy of strength, weight, and height ratings of 182 2D and 3D face images taken simultaneously. Strength, height and weight were rated by 66, 59 and 52 raters respectively, who viewed both 2D and 3D images. Results In line with previous studies, we found that weight and height can be judged somewhat accurately from faces; contrary to previous research, we found that people were relatively inaccurate at assessing strength. We found no evidence that physical characteristics could be judged more accurately from 3D than 2D images. Conclusion Our results suggest physical characteristics are perceived with similar accuracy from 2D and 3D face images. They also suggest that the substantial costs associated with collecting 3D face scans may not be justified for research on the accuracy of facial judgments of physical characteristics.

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Feasibility and accuracy of the automated software for dynamic quantification of left ventricular and atrial volumes and function in a large unselected population

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeaa356.356 ◽

2021 ◽

Vol 22 (Supplement_1) ◽

Author(s):

G Italiano ◽

G Tamborini ◽

V Mantegazza ◽

V Volpato ◽

L Fusini ◽

...

Keyword(s):

Machine Learning ◽

Atrial Fibrillation ◽

Image Quality ◽

Normal Subjects ◽

Left Ventricular ◽

Systolic Volume ◽

Unselected Population ◽

Lv Volumes ◽

Time Required ◽

2D Images

Abstract Funding Acknowledgements Type of funding sources: None. Objective. Preliminary studies showed the accuracy of machine learning based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes. We aimed to evaluate the feasibility and accuracy of machine learning based automated dynamic quantification of LV and LA volumes in an unselected population. Methods. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV and LA volumes were obtained from 2D images; 3D images were analysed using Dynamic Heart Model (DHM) software (Philips) resulting in LV and LA volume-time curves. A subgroup of 140 patients underwent also cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded and results were compared between 2DTTE, DHM and CMR. Results. The use of DHM was feasible in 522/600 cases (87%). When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) borders corrections were needed. The overall time required for DHM datasets was approximately 40 seconds, resulting in physiologically appearing LV and LA volume–time curves in all cases. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55%±12 vs 56%±14; LA volume 89 ± 36 vs 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0.70 and r = 0.82, p < 0.001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r= 0.82, p < 0.001) and LA volumes. Conclusions. The DHM software is feasible, accurate and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation. Table 1 DHM quality Adjustment Feasibility Good Suboptimal Minor Major Total of patients (n, %) 522/600 (87%) 327/522 (62%) 195/522 (28%) 149/522 (29%) 38/522 (6%) Normal subjects (n, %) 39/40 (97%) 23/39 (57%) 16/39 (40%) 9/39 (21%) 1/39 (3%) Atrial Fibrillation (n, %) 59/73 (81%)* 28/59 (47%) 31/59 (53%) 15/59 (25%) 6/59 (10%) Valvular disease (n, %) 271/312 (87%) 120/271 (%) 151/271 (%) 65/271 (24%) 16/271 (6%) Coronary artery disease (n, %) 47/58 (81%)* 26/47 (46%) 21/47 (37%) 16/47 (34%) 5/47 (11%) Miscellaneous (n, %) 24/25 (96%) 18/24 (75%) 6/24 (25%) 5/24 (21%) 3/24 (12%) Feasibility of DHM, image quality and need to adjustments in global population and in each subgroup. Abstract Figure 1

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Association between visual field damage and corneal structural parameters

Scientific Reports ◽

10.1038/s41598-021-90298-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alexandru Lavric ◽

Valentin Popa ◽

Hidenori Takahashi ◽

Rossen M. Hazarbassanov ◽

Siamak Yousefi

Keyword(s):

Machine Learning ◽

Visual Field ◽

Structural Parameters ◽

Corneal Thickness ◽

Anterior Segment ◽

Mean Deviation ◽

Automated Perimetry ◽

Standard Automated Perimetry ◽

Visual Field Damage ◽

Corneal Shape

AbstractThe main goal of this study is to identify the association between corneal shape, elevation, and thickness parameters and visual field damage using machine learning. A total of 676 eyes from 568 patients from the Jichi Medical University in Japan were included in this study. Corneal topography, pachymetry, and elevation images were obtained using anterior segment optical coherence tomography (OCT) and visual field tests were collected using standard automated perimetry with 24-2 Swedish Interactive Threshold Algorithm. The association between corneal structural parameters and visual field damage was investigated using machine learning and evaluated through tenfold cross-validation of the area under the receiver operating characteristic curves (AUC). The average mean deviation was − 8.0 dB and the average central corneal thickness (CCT) was 513.1 µm. Using ensemble machine learning bagged trees classifiers, we detected visual field abnormality from corneal parameters with an AUC of 0.83. Using a tree-based machine learning classifier, we detected four visual field severity levels from corneal parameters with an AUC of 0.74. Although CCT and corneal hysteresis have long been accepted as predictors of glaucoma development and future visual field loss, corneal shape and elevation parameters may also predict glaucoma-induced visual functional loss.

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Analysis of the performance of machine learning and deep learning methods for sex estimation of infant individuals from the analysis of 2D images of the ilium

International Journal of Legal Medicine ◽

10.1007/s00414-021-02660-6 ◽

2021 ◽

Author(s):

Raúl Fernández Ortega ◽

Javier Irurita ◽

Enrique José Estévez Campo ◽

Pablo Mesejo

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sex Estimation ◽

Learning Methods ◽

2D Images

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Comparing Reliability Between 3D Imaging And 2D Photography For External Nasal Anthropometry

10.21203/rs.3.rs-669579/v1 ◽

2021 ◽

Author(s):

Yoon-Soo Seo ◽

Ki-Hun Jo ◽

Jun-Sang CHA ◽

Joo-Yeon Kim ◽

Jae-Hwan Kwon

Keyword(s):

3D Imaging ◽

Intraclass Correlation ◽

Digital Camera ◽

Angular Parameter ◽

Good Reliability ◽

Rater Reliability ◽

3D Images ◽

Imaging Result ◽

External Nose ◽

2D And 3D

Abstract Background: This study investigates and compare the reliability and reproducibility of two facial anthropometric methods about external nasal angles, 3D imaging and conventional 2D photography. Methods: 2D photograph images and 3D images about external nose of 30 volunteers were taken using digital camera and Morpheus 3D scanner. To evaluate intra-rater reliability, each images were taken over two different days for each subject by the same researcher. To evaluate inter-rater reliability, another researcher took each images for each subject on the first day. The reliability of each method for measuring 4 external nasal angle is obtained using intraclass correlation coefficient (ICC) and compared.Results: Inter-rater and intra-rater reliability of both 3D imaging and 2D photography had excellent agreement in all 4 nasal angles. In the nasofacial angular parameter, Inter-rater ICC, 2D photography was significantly higher than 3D imaging. Result of intra-rater ICC also showed both 3D imaging and 2D photography had good reliability in all 4 nasal angles. Similar to those of inter-rater ICC, nasofacial angular parameter showed statistically significant differences between 3D imaging and 2D photography.Conclusion: In terms of reliability, both 2D and 3D showed appropriate anthropometric results and considering its own advantage, each methods can be used complementarily.

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