Combined Rotating Ultra-High-Resolution Spectral Domain OCT and Scheimpflug Imaging for In Vivo Corneal Optical Biopsy.

PURPOSE: This article introduces the Pentacam® Cornea OCT (optical coherence tomography). This advanced corneal imaging system combines rotating ultra-high-resolution spectral domain OCT with sub- 2-micron axial resolution and Scheimpflug photography. The purpose of this study is to present the first experience with the instrument and its potential for corneal diagnostics, including optical biopsy. METHODS: In this prospective study, the Pentacam® Cornea OCT was used to image the corneas of seven patients. The novel wide-angle pericentric scan system enables optimal OCT imaging performance for the corneal layer structure over the entire width of the cornea, including the limbal regions. A detailed analysis of the resulting images assessed the synergism between the OCT and Scheimpflug photography. RESULTS: The Pentacam® Cornea OCT demonstrated significantly improved image resolution and ability to individualize corneal layers with high quality. There is a synergism between the OCT high-definition signal to individualize details on the cornea and Scheimpflug photography to detect and quantify corneal scattering. The noncontact exam was proven safe, user-friendly, and effective for enabling optical biopsy. CONCLUSIONS: Pentacam® Cornea OCT is an advancement in corneal imaging technology. The ultra-high-resolution spectral domain OCT and Scheimpflug photography provide unprecedented detail and resolution, enabling optical biopsy and improving the understanding of corneal pathology. Further studies are necessary to compare and analyze the tomographic reconstructions of the cornea with the different wavelengths, which may provide helpful information for diagnosing and managing corneal diseases.

Keratoconus and Corneal Ectasia with Relatively Low Keratometry.

INTRODUCTION: The study aims to demonstrate and estimate the prevalence of clinical corneal ectasia and keratoconus (KC) in patients with relatively low keratometry (low-K KC). METHODS: In a retrospective, analytical, and non-interventionist study, one eye was randomly selected from 1054 patients from the original Tomographic Biomechanical Index (TBIv1) study and the external validation (from Rio de Janeiro, Brazil, and Milan, Italy clinics). Patients were stratified into three groups. Group 1 included 736 normal patients, and groups 2 and 3 included 318 patients with clinical KC in both eyes, divided into low-K KC (90 patients) and high-K KC (228 patients), respectively. All patients underwent a comprehensive ophthalmological evaluation along with Pentacam and Corvis ST (Oculus, Wetzlar, Germany) examinations. Cases with maximum mean zone 3 mm keratometry (Kmax zone mean 3 mm) lower than 47.6 diopters (D) were considered as low-keratometry keratoconus, and cases with Kmax zone mean 3 mm higher than 47.6 D were regarded as high-keratometry keratoconus. RESULTS: Ninety (28.30%) of the 318 KC group presented ectasia with low-keratometric values (low-Kmax). The average age in the normal group was 39.28 years (range 6.99-90.12), in the low-Kmax KC group it was 37.49 (range 13.35-78.45), and in the high-Kmax KC group it was 34.22 years (range 12.7-80.34). Mean and SD values and median (range), respectively, of some corneal tomographic and biomechanical parameters evaluated from the low-Kmax KC group were as follows: Belin-Ambrósio enhanced ectasia display (BAD-D) 3.79 ± 1.62 and 3.66 (0.83-9.73); Pentacam random forest index (PRFI) 0.78 ± 0.25 and 0.91 (0.05-1); corneal biomechanical index (CBI) 0.58 ± 0.43 and 0.75 (0-1); TBI 0.93 ± 0.17 and 1 (0.35-1); and stiffness parameter at A1 (SP-A1) 86.16 ± 19.62 and 86.05 (42.94-141.66). CONCLUSION: Relatively low keratometry, with a Kmax lower than 47.6 D, can occur in up to 28.30% of clinical keratoconus. These cases have a less severe presentation of the disease. Future studies involving larger populations and prospective designs are necessary to confirm the prevalence of keratoconus with low keratometry and define prognostic factors in such cases.

Multimodal diagnostics for keratoconus and ectatic corneal diseases: a paradigm shift.

Different diagnostic approaches for ectatic corneal diseases (ECD) include screening, diagnosis confirmation, classification of the ECD type, severity staging, prognostic evaluation, and clinical follow-up. The comprehensive assessment must start with a directed clinical history. However, multimodal imaging tools, including Placido-disk topography, Scheimpflug three-dimensional (3D) tomography, corneal biomechanical evaluations, and layered (or segmental) tomography with epithelial thickness by optical coherence tomography (OCT), or digital very high-frequency ultrasound (dVHF-US) serve as fundamental complementary exams for measuring different characteristics of the cornea. Also, ocular wavefront analysis, axial length measurements, corneal specular or confocal microscopy, and genetic or molecular biology tests are relevant for clinical decisions. Artificial intelligence enhances interpretation and enables combining such a plethora of data, boosting accuracy and facilitating clinical decisions. The applications of diagnostic information for individualized treatments became relevant concerning the therapeutic refractive procedures that emerged as alternatives to keratoplasty. The first paradigm shift concerns the surgical management of patients with ECD with different techniques, such as crosslinking and intrastromal corneal ring segments. A second paradigm shift involved the quest for identifying patients at higher risk of progressive iatrogenic ectasia after elective refractive corrections on the cornea. Beyond augmenting the sensitivity to detect very mild (subclinical or fruste) forms of ECD, ectasia risk assessment evolved to characterize the inherent susceptibility for ectasia development and progression. Furthermore, ectasia risk is also related to environmental factors, including eye rubbing and the relational impact of the surgical procedure on the cornea.

Decision taking in corneal refractive surgery.

A 27-year-old woman who wants to get rid of contact lenses and spectacles was seen at our clinic. She had strabismus surgery as a child and was patched for the right eye but now shows mild nondisturbing exophoria. Infrequently, she likes to box in the sports school. Her corrected distance visual acuity at presentation in the right eye was 20/16 with -3.75 -0.75 × 50 and in the left eye 20/16 with -3.75 -1.25 × 142. Her cycloplegic refraction in the right eye was -3.75 -0.75 × 44 and in the left eye was -3.25 -1.25 × 147. The left eye is the dominant eye. The tear break-up time was 8 seconds in both eyes, and the Schirmer tear test was 7 to 10 mm in right and left eyes, respectively. Pupil sizes under mesopic conditions were 6.62 mm and 6.68 mm. The anterior chamber depth (ACD) (measured from the epithelium) in the right eye was 3.89 mm and in the left eye was 3.87 mm. The corneal thickness was 503 µm and 493 µm of the right and left eye, respectively. Corneal endothelial cell density was on average 2700 cells/mm2 for both eyes. Slitlamp biomicroscopy showed clear corneas and a normal flat iris configuration. Supplemental Figures 1 to 4 (available at http://links.lww.com/JRS/A818, http://links.lww.com/JRS/A819, http://links.lww.com/JRS/A820, and http://links.lww.com/JRS/A821) show the corneal topography and Belin-Ambrósio deviation (BAD) maps at presentation of the right eye and left eye, respectively. Would you consider this patient a candidate for corneal refractive surgery (eg, laser-assisted subepithelial keratectomy, laser in situ keratomileusis [LASIK], or small-incision lenticule extraction [SMILE] procedure)? Has your opinion changed given the recent opinion of the U.S. Food and Drug Administration (FDA) regarding LASIK?1 The patient herself is slightly favoring an implantation of a phakic intraocular lens (pIOL), as she prefers something reversible. Would you implant a pIOL, and which type of IOL, for this level of myopia? What is your diagnosis or are additional diagnostic methodologies needed to establish a diagnosis? What is your treatment advice for this patient? REFERENCES 1. U.S. Food and Drug Administration, HHS. Laser-assisted in situ keratomileusis (LASIK) lasers-patient labeling recommendations; draft guidance for industry and food and drug administration staff; availability. July 28, 2022, Federal Register; 87 FR 45334. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/laser-assisted-situ-keratomileusis-lasik-lasers-patient-labeling-recommendations Accessed January 25, 2023.

Biomechanics in Keratoconus Diagnosis.

Purpose: To prospectively review the importance of biomechanical assessment in the screening, diagnosis, prognosis, individualized planning, and clinical follow-up for ectatic corneal diseases.Methods: We demonstrate two commercially available devices to assess the corneal biomechanics in vivo, the Ocular Response Analyzer (ORA, Reichester, NY, USA) and the Corvis ST (Oculus, Wetzlar, Germany). Novel devices have been demonstrated to provide in vivo biomechanical measurements, including Brillouin optical microscopy and OCT elastography. Conclusion: The integration of biomechanical data and other data from multimodal refractive imaging using artificial intelligence demonstrated the ability to enhance accuracy in diagnosing ectatic corneal diseases.

Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data.

PURPOSE: To optimize artificial intelligence (AI) algorithms to integrate Scheimpflug-based corneal tomography and biomechanics to enhance ectasia detection. DESIGN: Multicenter cross-sectional case-control retrospective study. METHODS: A total of 3886 unoperated eyes from 3412 patients had Pentacam and Corvis ST (Oculus Optikgeräte GmbH) examinations. The database included 1 eye randomly selected from 1680 normal patients (N) and from 1181 "bilateral" keratoconus (KC) patients, along with 551 normal topography eyes from patients with very asymmetric ectasia (VAE-NT), and their 474 unoperated ectatic (VAE-E) eyes. The current TBIv1 (tomographic-biomechanical index) was tested, and an optimized AI algorithm was developed for augmenting accuracy. RESULTS: The area under the receiver operating characteristic curve (AUC) of the TBIv1 for discriminating clinical ectasia (KC and VAE-E) was 0.999 (98.5% sensitivity; 98.6% specificity [cutoff: 0.5]), and for VAE-NT, 0.899 (76% sensitivity; 89.1% specificity [cutoff: 0.29]). A novel random forest algorithm (TBIv2), developed with 18 features in 156 trees using 10-fold cross-validation, had a significantly higher AUC (0.945; DeLong, P < .0001) for detecting VAE-NT (84.4% sensitivity and 90.1% specificity; cutoff: 0.43; DeLong, P < .0001) and a similar AUC for clinical ectasia (0.999; DeLong, P = .818; 98.7% sensitivity; 99.2% specificity [cutoff: 0.8]). Considering all cases, the TBIv2 had a higher AUC (0.985) than TBIv1 (0.974; DeLong, P < .0001). CONCLUSIONS: AI optimization to integrate Scheimpflug-based corneal tomography and biomechanical assessments augments accuracy for ectasia detection, characterizing ectasia susceptibility in the diverse VAE-NT group. Some patients with VAE may have true unilateral ectasia. Machine learning considering additional data, including epithelial thickness or other parameters from multimodal refractive imaging, will continuously enhance accuracy. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Corneal ectasia detection by epithelial pattern standard deviation from OCT.

PURPOSE: To test the ability of the corneal epithelial pattern standard deviation (PSD) to distinguish between normal and cases with corneal ectatic condition. SETTING: Instituto de Olhos Renato Ambrósio, Rio de Janeiro, Brazil. DESIGN: Cross-sectional retrospective study. METHODS: Patients were stratified into 4 groups based on clinical data and corneal tomography. Groups 1 and 2 comprised 1 eye randomly selected from 105 patients with normal corneas (N) and 86 patients with bilateral keratoconus (KC). Groups 3 and 4, respectively, comprised 11 ectatic eyes with no surgical treatment for KC (very asymmetric ectasia [VAE]-E) from patients whose fellow eyes (61) presented with normal topographic patterns (VAE-NT). Corneas were scanned using an OCT system (RT Vue) and Scheimpflug corneal tomography (Pentacam) and also had biomechanical assessment through the Corvis ST. Corneal epithelial thickness maps were analyzed, and the PSD value was calculated. The area under the receiver operating characteristic curve analysis was used to evaluate the diagnostic accuracy of the indices. RESULTS: A total of 105 normal eyes, 86 keratoconic eyes, and 11 ectatic eyes whose fellow eyes (61) presented normal topographic patterns were evaluated. Epithelial PSD was significantly different across the 4 groups ( P < .0001). The pairwise comparison revealed that the normal group presented significantly lower values than both ectasia groups (KC and VAE-E, P < .0001) and the VAE-NT group ( P = .0008). There was no statistical significant difference between KC and VAE-E ( P = .4284), while they were significantly higher than the VAE-NT group ( P < .0001 and P = .0004). CONCLUSIONS: Epithelial PSD can be used to detect abnormal epithelial thickness patterns. Corneal epithelial thickness changes could be detected accurately in patients with KC, even in the form fruste of the disease.

Enhanced Diagnostics for Corneal Ectatic Diseases: The Whats, the Whys, and the Hows.

There are different fundamental diagnostic strategies for patients with ectatic corneal diseases (ECDs): screening, confirmation of the diagnosis, classification of the type of ECD, severity staging, prognostic assessment, and clinical follow-up. The conscious application of such strategies enables individualized treatments. The need for improved diagnostics of ECD is related to the advent of therapeutic refractive procedures that are considered prior to keratoplasty. Among such less invasive procedures, we include corneal crosslinking, customized ablations, and intracorneal ring segment implantation. Besides the paradigm shift in managing patients with ECD, enhancing the sensitivity to detect very mild forms of disease, and characterizing the inherent susceptibility for ectasia progression, became relevant for identifying patients at higher risk for progressive iatrogenic ectasia after laser vision correction (LVC). Moreover, the hypothesis that mild keratoconus is a risk factor for delivering a baby with Down's syndrome potentially augments the relevance of the diagnostics of ECD. Multimodal refractive imaging involves different technologies, including Placido-disk corneal topography, Scheimpflug 3-D tomography, segmental or layered tomography with layered epithelial thickness using OCT (optical coherence tomography), and digital very high-frequency ultrasound (VHF-US), and ocular wavefront. Corneal biomechanical assessments and genetic and molecular biology tests have translated to clinical measurements. Artificial intelligence allows for the integration of a plethora of clinical data and has proven its relevance in facilitating clinical decisions, allowing personalized or individualized treatments.

Corneal biomechanics for corneal ectasia: Update.

Knowledge of biomechanical principles has been applied in several clinical conditions, including correcting intraocular pressure measurements, planning and following corneal treatments, and even allowing an enhanced ectasia risk evaluation in refractive procedures. The investigation of corneal biomechanics in keratoconus (KC) and other ectatic diseases takes place in several steps, including screening ectasia susceptibility, the diagnostic confirmation and staging of the disease, and also clinical characterization. More recently, investigators have found that the integration of biomechanical and tomographic data through artificial intelligence algorithms helps to elucidate the etiology of KC and ectatic corneal diseases, which may open the door for individualized or personalized medical treatments in the near future. The aim of this article is to provide an update on corneal biomechanics in the screening, diagnosis, staging, prognosis, and treatment of KC.

New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas.

PURPOSE: To assess the efficiency of an index derived from multiple logistic regression analysis (MLRA) to measure differences in corneal tomography findings between subclinical keratoconus (KC) in 1 eye, corneal ectasia, and healthy corneas. SETTING: 2 private Brazilian ophthalmological centers. DESIGN: Multicenter case-control study. METHODS: This study included 187 eyes with very asymmetric ectasia and with normal corneal topography and tomography (VAE-NTT) in the VAE-NTT group, 2296 eyes with healthy corneas in the control group (CG), and 410 eyes with ectasia in the ectasia group. An index, termed as Boosted Ectasia Susceptibility Tomography Index (BESTi), was derived using MLRA to identify a cutoff point to distinguish patients in the 3 groups. The groups were divided into 2 subgroups with an equal number of patients: validation set and external validation (EV) set. RESULTS: 2893 patients with 2893 eyes were included. BESTi had an area under the curve (AUC) of 0.91 with 86.02% sensitivity (Se) and 83.97% specificity (Sp) between CG and the VAE-NTT group in the EV set, which was significantly greater than those of the Belin-Ambrósio Deviation Index (BAD-D) (AUC: 0.81; Se: 66.67%; Sp: 82.67%; P < .0001) and Pentacam random forest index (PRFI) (AUC: 0.87; Se: 78.49%; Sp: 79.88%; P = .021). CONCLUSIONS: BESTi facilitated early detection of ectasia in subclinical KC and demonstrated higher Se and Sp than PRFI and BAD-D for detecting subclinical KC.

Corneal deformation amplitude analysis for keratoconus detection through compensation for intraocular pressure and integration with horizontal thickness profile.

BACKGROUND: The Corvis ST provides measurements of intraocular pressure (IOP) and a biomechanically-corrected IOP (bIOP). IOP influences corneal deflection amplitude (DA), which may affect the diagnosis of keratoconus. Compensating for IOP in DA values may improve the detection of keratoconus. METHODS: 195 healthy eyes and 136 eyes with keratoconus were included for developing different approaches to distinguish normal and keratoconic corneas using attribute selection and discriminant function. The IOP compensation is proposed by dividing the DA by the IOP values. The first approaches include DA compensated for either IOP or bIOP and other parameters from the deformation corneal response (DCR). Another approach integrated the horizontal corneal thickness profile (HCTP). The best classifiers developed were applied in a validation database of 156 healthy eyes and 87 eyes with keratoconus. Results were compared with the current Corvis Biomechanical Index (CBI). RESULTS: The best biomechanical approach used the DA values compensated by IOP (Approach 2) using a linear discriminant function and reached AUC 0.954, with a sensitivity of 88.2% and a specificity of 97.4%. When thickness horizontal profile data was integrated (Approach 4), the best function was the diagquadratic, resulting in an AUC of 0.960, with a sensitivity of 89.7% and a specificity of 96.4%. There was no significant difference in the results between approaches 2 and 4 with the CBI in the training and validation databases. CONCLUSIONS: By compensating for the IOP, and with the horizontal thickness profile included or excluded, it was possible to generate a classifier based only on biomechanical information with a similar result to the CBI.

Enhanced Tomographic Assessment to Detect Corneal Ectasia Based on Artificial Intelligence.

PURPOSE: To improve the detection of corneal ectasia susceptibility using tomographic data. DESIGN: Multicenter case-control study. METHODS: Data from patients from 5 different clinics from South America, the United States, and Europe were evaluated. Artificial intelligence (AI) models were generated using Pentacam HR (Oculus, Wetzlar, Germany) parameters to discriminate the preoperative data of 3 groups: stable laser-assisted in situ keratomileusis (LASIK) cases (2980 patients with minimum follow-up of 7 years), ectasia susceptibility (71 eyes of 45 patients that developed post-LASIK ectasia [PLE]), and clinical keratoconus (KC; 182 patients). Model accuracy was independently tested in a different set of stable LASIK cases (298 patients with minimum follow-up of 4 years) and in 188 unoperated patients with very asymmetric ectasia (VAE); these patients presented normal topography (VAE-NT) in 1 eye and clinically diagnosed ectasia in the other (VAE-E). Accuracy was evaluated with ROC curves. RESULTS: The random forest (RF) provided highest accuracy among AI models in this sample with 100% sensitivity for clinical ectasia (KC+VAE-E; cutoff 0.52), being named Pentacam Random Forest Index (PRFI). Considering all cases, the PRFI had an area under the curve (AUC) of 0.992 (94.2% sensitivity, 98.8% specificity; cutoff 0.216), being statistically higher than the Belin/Ambrósio deviation (BAD-D; AUC = 0.960, 87.3% sensitivity, 97.5% specificity; P = .006, DeLong's test). The optimized cutoff of 0.125 provided sensitivity of 85.2% for VAE-NT and 80% for PLE, with 96.6% specificity. CONCLUSION: The PRFI enhances ectasia diagnosis. Further integrations with corneal biomechanical parameters and with the corneal impact from laser vision correction are needed for assessing ectasia risk.

Computational Models for Optimization of the Intrastromal Corneal Ring Choice in Patients With Keratoconus Using Corneal Tomography Data.

PURPOSE: To evaluate the predictability of asphericity and average keratometry in patients with keratoconus after implantation of intrastromal corneal ring segments (ICRS) using artificial intelligence. METHODS: This study included 209 eyes of 160 patients with keratoconus (grades I, II, and III) who had ICRS implanted. The 160 arc length Ferrara ICRS was implanted in all patients. ICRS thickness varied from 150 to 250 µm. Pentacam (Oculus Optikgeräte, Wetzlar, Germany) corneal tomography parameters, clinical data, and ICRS data formed the basis of the 39 studied parameters. Linear regression was used to create the models. RESULTS: The best mean absolute error value found was 0.19 for asphericity and was 1.18 for mean keratometry. Comparing the mean absolute error values of the nomogram with the average absolute error of the algorithm, there was an improvement of 0.11 for asphericity and 0.09 for mean keratometry in relation to the current nomogram. CONCLUSIONS: The current study showed that the use of computational models could lead to more accurate results and contribute to better surgical decision-making to improve the clinical outcomes in patients with keratoconus implanted with ICRS. [J Refract Surg. 2018;34(8):547-550.].

Tomographic Findings After Implantation of Ferrara Intrastromal Corneal Ring Segments in Keratoconus.

PURPOSE: To analyze and compare changes to the anterior and posterior corneal surfaces after Ferrara intracorneal ring segment (ICRS) (AJL, Boecillo, Spain) implantation and to correlate those changes with visual outcomes. METHODS: This retrospective case series study comprised consecutive patients with keratoconus implanted with the Ferrara ICRS. Computed tomography scans of the two corneal surfaces were obtained preoperatively and postoperatively with a rotating Scheimpflug imaging system. The Wilcoxon test was used to compare groups, and the Pearson's correlation coefficient was used to measure the strength of the correlation between variables. RESULTS: The study evaluated 241 eyes in 182 patients with keratoconus. Both corneal surfaces showed statistically significant decreases in average steep keratometry (K2) values, corneal astigmatism, elevation at the apex and at the thinnest point, maximum elevation in the central 4-mm area, and elevation inside the 3- and 4-mm zones and at the 3-mm ring. Regarding anterior surface parameters, a significant decrease was observed in flat keratometry (K1), whereas asphericity increased. On the posterior corneal surface, no significant changes in K1 and asphericity were observed. Poor correlation was found between visual outcomes and changes in tomographic parameters. The best correlation was obtained for anterior corneal astigmatism. CONCLUSIONS: ICRS implantation led to statistically significant changes in both anterior and posterior corneal surfaces. However, correlation between visual outcomes and tomographic parameters was poor. [J Refract Surg. 2017;33(2):110-115.].

Automatic weighing attribute to retrieve similar lung cancer nodules.

BACKGROUND: Cancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung cancer is the primary cause of cancer-related deaths in the world, and it diagnosis is a complex task for specialists and it presents some big challenges as medical image interpretation process, pulmonary nodule detection and classification. In order to aid specialists in the early diagnosis of lung cancer, computer assistance must be integrated in the imaging interpretation and pulmonary nodule classification processes. Methods of Content-Based Image Retrieval (CBIR) have been described as one promising technique to computer-aided diagnosis and is expected to aid radiologists on image interpretation with a second opinion. However, CBIR presents some limitations: image feature extraction process and appropriate similarity measure. The efficiency of CBIR systems depends on calculating image features that may be relevant to the case similarity analysis. When specialists classify a nodule, they are supported by information from exams, images, etc. But each information has more or less weight over decision making about nodule malignancy. Thus, finding a way to measure the weight allows improvement of image retrieval process through the assignment of higher weights to that attributes that best characterize the nodules. METHODS: In this context, the aim of this work is to present a method to automatically calculate attribute weights based on local learning to reflect the interpretation on image retrieval process. The process consists of two stages that are performed sequentially and cyclically: Evaluation Stage and Training Stage. At each iteration the weights are adjusted according to retrieved nodules. After some iterations, it is possible reach a set of attribute weights that optimize the recovery of similar nodes. RESULTS: The results achieved by updated weights were promising because was possible increase precision by 10% to 6% on average to retrieve of benign and malignant nodules, respectively, with recall of 25% compared with tests without weights associated to attributes in similarity metric. The best result, we reaching values over 100% of precision average until thirtieth lung cancer nodule retrieved. CONCLUSIONS: Based on the results, WED applied to the three vectors used attributes (3D TA, 3D MSA and InV), with weights adjusted by the process, always achieved better results than those found with ED. With the weights, the Precision was increased on average by 17.3% compared with using ED.

Proposal of Local Automatic Weighing Attribute in CBIR.

Lung cancer is the most common malignant lesion and the principal cause of cancer-related death worldwide. This problem encourages researchers to build computer-aided solutions to help diagnose lung cancer. Content-based image retrieval (CBIR) systems are very promising in this context due to a large number of image generated everyday. However, semantic gaps have limited CBIR applicability. This work proposes a new approach to automatically adjust CBIR attribute weights to reflect users' semantic interpretation on retrieval process, minimizing the semantic gap problem and improving retrieval accuracy.

A model-Driven Approach to Customize the Vocabulary of Communication Boards: Towards More Humanization of Health Care.

OBJECTIVE: This work presents a Modeling Language and its technological infrastructure to customize the vocabulary of Communication Boards (CB), which are important tools to provide more humanization of health care. METHOD: Using a technological infrastructure based on Model-Driven Development (MDD) approach, our Modelin Language (ML) creates an abstraction layer between users (e.g., health professionals such as an audiologist or speech therapist) and application code. Moreover, the use of a metamodel enables a syntactic corrector for preventing creation of wrong models. RESULTS: Our ML and metamodel enable more autonomy for health professionals in creating customized CB because it abstracts complexities and permits them to deal only with the domain concepts (e.g., vocabulary and patient needs). Additionally, our infrastructure provides a configuration file that can be used to share and reuse models. This way, the vocabulary modelling effort will decrease our time since people share vocabulary models. CONCLUSION: Our study provides an infrastructure that aims to abstract the complexity of CB vocabulary customization, giving more autonomy to health professionals when they need customizing, sharing and reusing vocabularies for CB.

Intrastromal corneal ring segments implantation in patients with keratoconus: 10-year follow-up.

PURPOSE: To evaluate the long-term safety and efficacy of Ferrara intrastromal corneal ring segments (ICRS) (Ferrara Ring; AJL, Boecillo, Spain) in patients with keratoconus. METHODS: The chart records of 36 eyes of 30 patients with keratoconus implanted with ICRS, operated on between July 1996 and January 2002, were retrospectively reviewed. The following parameters were studied: uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), keratometry (K), and central corneal thickness. The outcomes were evaluated at 5 and 10 years after ICRS implantation. RESULTS: The mean UDVA (logMAR) improved from 1.01 ± 0.28 (20/200 Snellen) to 0.71 ± 0.38 (20/100 Snellen) at 5 years (P < .05) and 0.67 ± 0.25 (20/90 Snellen) at 10 years (P = .735). The mean CDVA (logMAR) improved from 0.45 ± 0.45 (20/55 Snellen) to 0.24 ± 0.19 (20/35 Snellen) at 5 years (P < .05) and 0.29 ± 0.09 (20/38 Snellen) at 10 years (P = .292). The mean maximum K value decreased from 54.99 ± 6.33 to 50.58 ± 5.11 D at 5 years (P < .05) and 50.65 ± 5.17 D at 10 years (P = .854). The mean minimum K value decreased from 48.85 ± 5.70 to 46.90 ± 5.08 D at 5 years (P < .05) and 47.12 ± 4.22 D at 10 years (P = .945). The central corneal thickness decreased from 457.42 ± 58.21 to 421.34 ± 74.12 µm at 5 years (P = .039) and 434.32 ± 77.65 µm at 10 years (P = .427). CONCLUSIONS: Intrastromal corneal ring segments can effectively improve UDVA and CDVA 10 years after implantation in patients with keratoconus.

Analysis of waveform-derived ORA parameters in early forms of keratoconus and normal corneas.

PURPOSE: To evaluate the Ocular Response Analyzer (ORA; Reichert Ophthalmic Instruments, Depew, NY) performance in differentiating grades I and II keratoconus from normal corneas using 41 parameters individually and to assess the effect of analyzing all parameters together. METHODS: This study compared the mean value of 41 ORA parameters in grades I and II keratoconus with healthy age-matched control eyes. Only eyes with a central corneal thickness between 500 and 600 µm were included. The area under the receiver operating characteristic curve was calculated for each of the 41 parameters independently and for all of the parameters together. RESULTS: This study included 136 eyes with normal corneas and 68 eyes with grades I and II keratoconus. When analyzed individually, four ORA parameters (p1area, p1area1, p2area, and p2area1) had an area under the curve greater than 0.900 for discriminating between both groups. The p2area was the parameter that achieved the largest area under the curve individually (0.931). The area under the curve increased to 0.978 when analyzing all parameters together. CONCLUSION: Alternative ORA parameters are better for differentiating grades I and II keratoconus from normal corneas than the four parameters originally available for ophthalmologists (corneal hysteresis, Goldmann-correlated intraocular pressure, corneal-compensated intraocular pressure, and corneal resistance factor). Although the ORA did not achieve 100% accuracy, the discrimination between these two groups was optimized by combining all parameters.

Intrastromal corneal ring segment implantation to correct astigmatism after penetrating keratoplasty.

PURPOSE: To evaluate the clinical outcomes of implantation of Ferrara intrastromal corneal ring segments (ICRS) in patients with astigmatism after penetrating keratoplasty (PKP). SETTING: Private clinic, Belo Horizonte, Brazil. DESIGN: Retrospective consecutive case series. METHODS: Chart records of post-PKP patients who had ICRS implantation from May 2005 to September 2009 were retrospectively reviewed. The following parameters were studied: corrected distance visual acuity (CDVA), keratometry (K) values, spherical equivalent (SE), spherical refractive error, corneal topographic astigmatism, minimum K, and maximum K. RESULTS: The study evaluated 59 eyes (54 patients). The mean CDVA (logMAR) improved from 0.45 ± 0.17 (SD) (range 0.18 to 1.00) to 0.30 ± 0.17 (range 0.00 to 1.00). The mean SE was -6.34 ± 3.40 diopters (D) (range 0.37 to -16.50 D) preoperatively and -2.66 ± 2.52 D (range 0.87 to -10.50 D) postoperatively. The mean spherical refractive error decreased from -7.10 ± 3.07 D (range 2.15 to 16.68 D) preoperatively to -3.46 ± 2.05 D (range 0.88 to 10.79 D) postoperatively. No patient lost visual acuity. The mean corneal topographic astigmatism decreased from 3.37 ± 1.51 D preoperatively to 1.69 ± 1.04 D postoperatively. The mean maximum K value decreased from 48.09 ± 2.56 D to 44.17 ± 2.67 D and the mean minimum K value, from 44.90 ± 2.54 D to 42.46 ± 2.63 D. All changes were statistically significant (P<.0001). CONCLUSION: Intrastromal corneal ring segments effectively reduced corneal cylinder in patients with astigmatism after PKP.