Submit Article
Asian Journal of Physics Vol 32, Nos 3 & 4 (2023) 185-204

Clinical validation of an automatic system to categorize tear film lipid layer patterns

M J Giráldez, C García-Resúa, H Pena-Verdeal, J Garcia-Queiruga, and E Yebra-Pimentel
Departament of Applied Physics (Optometry Area), Universidade de Santiago de Compostela, Spain
Dedicated to Prof Jay M Enoch

This paper deals with validation of clinical performance of the objective application iDEAS (Dry Eye Assessment System) to categorize different zones of tear film lipid layer patterns (LLPs). All procedures followed the Declaration of Helsinki, and the protocol was reviewed and approved by the Ethics Committee of the University of Santiago de Compostela. The authors report no conflicts of interest and have no proprietary interest in any of the materials mentioned in this article. © Anita Publications. All rights reserved.
Keywords: Microsaccades, Preferential looking, Visual search, Latency, Priming, Eye movements.

Peer Review Information
Method: Single- anonymous; Screened for Plagiarism? Yes
Buy this Article in Print © Anita Publications. All rights reserve

References

  1. Craig J P, Nichols K K, Akpek E K, Caffery B, Dua H S, Joo CK, Liu Z, Nelson J D, Nichols J J, Tsubota K, Stapleton F, TFOS DEWS II Definition and Classification Report, Ocul Surf, 15(2017)276–83.
  2. Khanal S, Tomlinson A, McFadyen A, Diaper C, Ramaesh K, Dry eye diagnosis, Invest Ophthalmol Vis Sci, 1407–1414.
  3. Nichols K K, Nichols J J, Mitchell G L, The lack of association between signs and symptoms in patients with dry eye disease, Cornea, 23(2004)762–770.
  4. Geerling G, Tauber J, Baudouin C, Goto E, Matsumoto Y, O’Brien T, Rolando M, Tsubota K, Nichols K K, The international workshop on meibomian gland dysfunction: report of the subcommittee on management and treatment of meibomian gland dysfunction. Invest Ophthalmol Vis Sci, 52(2011)2050–2064.
  5. Tomlinson A, Blades K J, Pearce E I, What does the phenol red thread test actually measure? Optom Vis Sci, 78(2001)142–146.
  6. Wolffsohn J S, Arita R, Chalmers R, Djalilian A, Dogru M, Dumbleton K, Gupta P K, Karpecki P, Lazreg S. TFOS DEWS II Diagnostic Methodology report, Ocul Surf, 15(2017)539–574.
  7. Wolffsohn J S, Purslow C, Clinical monitoring of ocular physiology using digital image analysis, Cont Lens Anterior Eye, 26(2003)27–35.
  8. Fieguth P, Simpson T. Automated measurement of bulbar redness, Invest Ophthalmol Vis Sci, 43(2002)340–347.
  9. Ramos L, Barreira N, Mosquera A, Penedo M G, Yebra-Pimentel E, Garcia-Resua C. Analysis of parameters for the automatic computation of the tear film break-up time test based on CCLRU standards, Comput Methods Programs Biomed, 113(2014)715–724.
  10. Pult H, Riede-Pult B, Comparison of subjective grading and objective assessment in meibography, Cont Lens Anterior Eye, 36(2013)22–27.
  11. Pena-Verdeal H, Ramos L, Garcia-Queiruga J, Garcia-Resua C, Giraldez M J, Yebra-Pimentel E, Validation of a New Software Application for Tear Breakup Measurement, Optom Vis Sci, 99(2022)159–166.
  12. Ramos L, Barreira N, Pena-Verdeal H, Giraldez M J, Automatic assessment of tear film break-up dynamics, Stud Health Technol Inform, 207(2014)173–182.
  13. Sanchez Brea M L, Barreira Rodriguez N, Mosquera Gonzalez A, Evans K, Pena-Verdeal H, Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva, Comput Math Methods Med, 2016(2016)3695014; doi.org/10.1155/2016/3695014.
  14. Nichols K K, The international workshop on meibomian gland dysfunction: introduction, Invest Ophthalmol Vis Sci, 52(2011)1917–1921.
  15. Nelson J D, Shimazaki J, Benitez-del-Castillo J M, Craig J P, McCulley J P, Den S, Foulks G N, The international workshop on meibomian gland dysfunction: report of the definition and classification subcommittee. Invest Ophthalmol Vis Sci, 52(2011)1930–1937.
  16. Matsumoto Y, Dogru M, Goto E, Ishida R, Kojima T, Onguchi T, Yukiko Y, Jun S, Kazuo T, Efficacy of a new warm moist air device on tear functions of patients with simple meibomian gland dysfunction, Cornea, 25(2006)644–650.
  17. Arita R, Itoh K, Maeda S, Maeda K, Furuta A, Fukuoka S, Tomidokoro A, Amano S, Proposed diagnostic criteria for obstructive meibomian gland dysfunction, Ophthalmology, 116(2009)2058-2063 e1.
  18. King-Smith P E, Ramamoorthy P, Braun R J, Nichols J J, Tear film images and breakup analyzed using fluorescent quenching, Invest Ophthalmol Vis Sci, 54(2013)6003–6011.
  19. Peng C C, Cerretani C, Braun R J, Radke C J, Evaporation-driven instability of the precorneal tear film. Advances in colloid and interface, Science, 206(2014)250–264.
  20. Guillon J P, Non-invasive Tearscope Plus routine for contact lens fitting, Contact Lens & Anterior Eye (Supplement), 21(1998)S31–S40.
  21. Rolando M, Valente C, Barabino S. New test to quantify lipid layer behavior in healthy subjects and patients with keratoconjunctivitis sicca, Cornea, 27(2008)866–870.
  22. Giraldez M J, Naroo S A, Resua C G. A preliminary investigation into the relationship between ocular surface temperature and lipid layer thickness, Contact Lens & Anterior Eye, 32(2009)177–180.
  23. Garcia-Resua C, Pena-Verdeal H, Minones M, Giraldez MJ, Yebra-Pimentel E. Interobserver and intraobserver repeatability of lipid layer pattern evaluation by two experienced observers, Contact Lens & Anterior Eye, J Br Contact Lens Assoc, 37(2014)431–437.
  24. Guillon J P, Abnormal lipid layers. Observation, differential diagnosis, and classification, Adv Exp Med Biol, 438(1998)309–313.
  25. Korb D R (ed), The tear film-its role today and in the future. In: The tear film, structure, function and clinical evaluation, (Butterworth-Heinemann), 2002, pp 126-192.
  26. Nichols J J, Nichols K K, Puent B, Saracino M, Mitchell G L, Evaluation of tear film interference patterns and measures of tear break-up time, Optom Vis Sci, 79(2002)363–369.
  27. King-Smith P E, Fink B A, Fogt N. Three interferometric methods for measuring the thickness of layers of the tear film, Optom Vis Sci, 76(1999)19–32.
  28. Goto E, Dogru M, Kojima T, Tsubota K. Computer-synthesis of an interference color chart of human tear lipid layer, by a colorimetric approach, Invest Ophthalmol Vis Sci, 44(2003)4693–4697.
  29. Garcia-Marques J V, Talens-Estarelles C, Garcia-Lazaro S, Cervino A, Validation of a new objective method to assess lipid layer thickness without the need of an interferometer, Graefes Arch Clin Exp Ophthalmol, 260(2022)655–676.
  30. Ting D S W, Tan T E, Lim C C T, Development and Validation of a Deep Learning System for Detection of Active Pulmonary Tuberculosis on Chest Radiographs: Clinical and Technical Considerations, Clin Infect Dis, 69(2019)748–750.
  31. Lakhani P, Sundaram B, Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks, Radiology, 284(2017)574–582.
  32. Remeseiro B, Bolon-Canedo V, Peteiro-Barral D, Alonso-Betanzos A, Guijarro-Berdinas B, Mosquera A, Penedo M, Sánchez-Maroño N, A methodology for improving tear film lipid layer classification, IEEE J Biomed Health Inform, 18(2014)1485–1493.
  33. Garcia-Resua C, Giraldez Fernandez M J, Gonzalez Penedo M F, Calvo D, Penas M, Yebra-Pimentel E, New software application for clarifying tear film lipid layer patterns, Cornea, 32(2013)538–546.
  34. Remeseiro B, Barreira N, Garcia-Resua C, Lira M, Giraldez M J, Yebra-Pimentel E, Penedo M, iDEAS: A web-based system for dry eye assessment, Comput Methods Programs Biomed, 130(2016)186–197.
  35. Hecht E, Optics. 4th edn, (Addison-Wesley, San Francisco), 2002.
  36. Guillon J P, Guillon M, The role of tears in contact lens performance and its measurement, In: Montague R, Guillon M, (eds), Contact lens practice, (Chapman and Hall Medical, London), 1994, pp 453–483.
  37. Aydogdu S, Ertekin K, Suslu A, Ozdemir M, Celik E, Cocen U, Optical CO2 sensing with ionic liquid doped electrospun nanofibers, J Fluoresc, 21(2011)607–613.
  38. Remeseiro B, Penas M, Mosquera A, Novo J, Penedo M G, Yebra-Pimentel E, Statistical comparison of classifiers applied to the interferential tear film lipid layer automatic classification, Comput Math Methods Med, 2012(2012)207315; doi.org/10.1155/2012/207315.
  39. Remeseiro B, Penas M, Barreira N, Mosquera A, Novo J, Garcia-Resua C. Automatic classification of the interferential tear film lipid layer using colour texture analysis, Comput Methods Programs Biomed, 111(2013)93–103.
  40. Remeseiro B, Ramos L, Barreira N, Mosquera A, Yebra-Pimentel E, Colour texture segmentation of tear film lipid layer images. In: Moreno-Díaz R, Pichler F, Quesada-Arencibia A (eds), Computer Aided Systems Theory, Lecture Notes in Computer Science, (Springer, Berlin), 2013, pp140–147.
  41. McLaren K. The development of the CIE 1976 (L*a*b) uniform colour-space and colour-difference formula, J Soc Dyers Colourists, 92(1976)338–341.
  42. Haralick R M, Shanmugam K, Dinstein I, Textural features for image classification, IEEE Transactions on Systems, Man and Cybernetics, SMC-3(1973)610–621.
  43. Maissa C, Guillon M, Tear film dynamics and lipid layer characteristics–effect of age and gender, Cont Lens Anterior Eye, 33(2010)176–182.
  44. Isenberg S J, Signore M D, Chen A, Wei J, Guillon J P, The lipid layer and stability of the preocular tear film in newborns and infants, Ophthalmology, 110(2003)1408–1411.
  45. Foulks G N, The correlation between the tear film lipid layer and dry eye disease, Surv Ophthalmol, 52(2007)369–374.
  46. Patel S, Wallace I, Tear meniscus height, lower punctum lacrimale, and the tear lipid layer in normal aging, Optom Vis Sci, 83(2006)731–739.
Clinical-validation-of-an-automatic-system-to-categorize-tear-film-lipid-layer-patternsDownload

Related Posts