The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Integration of Scheimpflug-Based Corneal Tomography and Biomechanical Assessments for Enhancing Ectasia Detection
Open accessAbstract
PURPOSE: To present the Tomographic and Biomechanical Index (TBI), which combines Scheimpflugbased corneal tomography and biomechanics for enhancing ectasia detection. METHODS: Patients from different continents were retrospectively studied. The normal group included 1 eye randomly selected from 480 patients with normal corneas and the keratoconus group included 1 eye randomly selected from 204 patients with keratoconus. There were two groups: 72 ectatic eyes with no surgery from 94 patients with very asymmetric ectasia (VAE-E group) and the fellow eyes of these patients with normal topography (VAE-NT group). Pentacam HR and Corvis ST (Oculus Optikgeräte GmbH, Wetzlar, Germany) parameters were analyzed and combined using different artificial intelligence methods. The accuracies for detecting ectasia of the Belin/Ambrósio Deviation (BAD-D) and Corvis Biomechanical Index (CBI) were compared to the TBI, considering the areas under receiver operating characteristic curves (AUROCs). RESULTS: The random forest method with leave-one-out cross-validation (RF/LOOCV) provided the best artificial intelligence model. The AUROC for detecting ectasia (keratoconus, VAE-E, and VAE-NT groups) of the TBI was 0.996, which was statistically higher (DeLong et al., P < .001) than the BAD-D (0.956) and CBI (0.936). The TBI cut-off value of 0.79 provided 100% sensitivity for detecting clinical ectasia (keratoconus and VAE-E groups) with 100% specificity. The AUROCs for the TBI, BAD-D, and CBI were 0.985, 0.839, and 0.822 in the VAE-NT group (DeLong et al., P < .001). An optimized TBI cut-off value of 0.29 provided 90.4% sensitivity with 96% specificity in the VAE-NT group. CONCLUSIONS: The TBI generated by the RF/LOOCV provided greater accuracy for detecting ectasia than other techniques. The TBI was sensitive for detecting subclinical (fruste) ectasia among eyes with normal topography in very asymmetric patients. The TBI may also confirm unilateral ectasia, potentially characterizing the inherent ectasia susceptibility of the cornea, which should be the subject of future studies. [ J Refract Surg. 2017;33(7):434–443.]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649
Rabinowitz, Y. S. (1998) · Survey of Ophthalmology · doi:10.1016/s0039-6257(97)00119-7 · Cited by 2,744
Global Consensus on Keratoconus and Ectatic Diseases
Gomes, J. A. P., Tan, D., Rapuano, C. J., Belin, M. W., Ambrósio, R., Guell, J. L., Malecaze, F., Nishida, K., & Sangwan, V. S. (2015) · Cornea · doi:10.1097/ico.0000000000000408 · Cited by 1,132
Self-citationDetection of Keratoconus With a New Biomechanical Index
Vinciguerra, R., Ambrósio, R., Elsheikh, A., Roberts, C. J., Lopes, B., Morenghi, E., Azzolini, C., & Vinciguerra, P. (2016) · Journal of Refractive Surgery · doi:10.3928/1081597x-20160629-01 · Cited by 502
Self-citationRabinowitz, Y. S., & Rasheed, K. (1999) · Journal of Cataract & Refractive Surgery · doi:10.1016/s0886-3350(99)00195-9 · Cited by 475
Arbelaez, M. C., Versaci, F., Vestri, G., Barboni, P., & Savini, G. (2012) · Ophthalmology · doi:10.1016/j.ophtha.2012.06.005 · Cited by 256
Detection of Subclinical Keratoconus Using an Automated Decision Tree Classification
Smadja, D., Touboul, D., Cohen, A., Doveh, E., Santhiago, M. R., Mello, G. R., Krueger, R. R., & Colin, J. (2013) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2013.03.034 · Cited by 241
Dynamic ultra high speed Scheimpflug imaging for assessing corneal biomechanical properties
Ambrósio Jr, R., Ramos, I., Luz, A., Faria, F. C., Steinmueller, A., Krug, M., Belin, M. W., & Roberts, C. J. (2013) · Revista Brasileira de Oftalmologia · doi:10.1590/s0034-72802013000200005 · Cited by 205
Self-citationCorneal Ectasia after Photorefractive Keratectomy for Low Myopia
Malecaze, F., Coullet, J., Calvas, P., Fournié, P., Arné, J.-L., & Brodaty, C. (2006) · Ophthalmology · doi:10.1016/j.ophtha.2005.11.023 · Cited by 104
Metadata
- Authors
- Ambrósio, R., Lopes, B. T., Faria-Correia, F., Salomão, M. Q., Bühren, J., Roberts, C. J., Elsheikh, A., Vinciguerra, R., & Vinciguerra, P.
- Venue
- Journal of Refractive Surgery
- Publisher
- SLACK, Inc.
- Published
- 2017-07
- Volume
- 33
- Issue
- 7
- Pages
- 434-443
- ISSN
- 1042-962X (ISSN-L)
- 1081-597X
- OpenAlex
- W2728958029
- Cited by
- 477
- References
- 11
- FWCI
- 21.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Ambrósio, R., Lopes, B. T., Faria-Correia, F., Salomão, M. Q., Bühren, J., Roberts, C. J., Elsheikh, A., Vinciguerra, R., & Vinciguerra, P. (2017). Integration of Scheimpflug-Based Corneal Tomography and Biomechanical Assessments for Enhancing Ectasia Detection. Journal of Refractive Surgery. https://doi.org/10.3928/1081597x-20170426-02
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