Diagnosis of response and non-response to dry eye treatment using infrared thermography images

Acharya, U. Rajendra; Tan, Jen Hong; Sudevan, Vidya; Yeo, Sharon; Too, Cheah Loon; Lim, Wei Jie Eugene; Chua, Kuang Chua; Tong, Louis

doi:10.1016/j.infrared.2014.09.011

Cited by 19 publications

(5 citation statements)

References 37 publications

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“…Recently, Acharya et al, have studied the responder analysis to evaluate the clinical significance on the effect of various dry eye treatments [68]. Their proposed method achieved an average accuracy, sensitivity and specificity of 99.8%, 99.7% and 100% respectively using KNN classifier.…”

Section: Discussionmentioning

confidence: 99%

Automated diagnosis of dry eye using infrared thermography images

Acharya

Tan

Koh

et al. 2015

Infrared Physics & Technology

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Section: Discussionmentioning

confidence: 99%

Automated diagnosis of dry eye using infrared thermography images

Acharya

Tan

Koh

et al. 2015

Infrared Physics & Technology

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“…In this work, texture methods [14][15][16][17] were used to extract the following features. One feature was obtained using Fourier Spectrum (FS), one with fractal dimension (FD), and a few others using the Gray Level Co-Occurrence matrix.…”

Section: Featuresmentioning

confidence: 99%

Longitudinal Changes in Tear Evaporation Rates After Eyelid Warming Therapies in Meibomian Gland Dysfunction

Yeo

Tan

Acharya

et al. 2016

Invest. Ophthalmol. Vis. Sci.

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Citation: Yeo S, Tan JH, Acharya UR, Sudarshan VK, Tong L. Longitudinal changes in tear evaporation rates after eyelid warming therapies in meibomian gland dysfunction. Invest Ophthalmol Vis Sci. 2016;57:197457: -198157: . DOI:10.1167 PURPOSE. Lid warming is the major treatment for meibomian gland dysfunction (MGD). The purpose of the study was to determine the longitudinal changes of tear evaporation after lid warming in patients with MGD.METHODS. Ninety patients with MGD were enrolled from a dry eye clinic at Singapore National Eye Center in an interventional trial. Participants were treated with hot towel (n ¼ 22), EyeGiene (n ¼ 22), or Blephasteam (n ¼ 22) twice daily or a single 12-minute session of Lipiflow (n ¼ 24). Ocular surface infrared thermography was performed at baseline and 4 and 12 weeks after the treatment, and image features were extracted from the captured images.RESULTS. The baseline of conjunctival tear evaporation (TE) rate (n ¼ 90) was 66.1 6 21.1 W/ min. The rates were not significantly different between sexes, ages, symptom severities, tear breakup times, Schirmer test, corneal fluorescein staining, or treatment groups. Using a general linear model (repeat measures), the conjunctival TE rate was reduced with time after treatment. A higher baseline evaporation rate ( ‡66 W/min) was associated with greater reduction of evaporation rate after treatment. Seven of 10 thermography features at baseline were predictive of reduction in irritative symptoms after treatment.CONCLUSIONS. Conjunctival TE rates can be effectively reduced by lid warming treatment in some MGD patients. Individual baseline thermography image features can be predictive of the response to lid warming therapy. For patients that do not have excessive TE, additional therapy, for example, anti-inflammatory therapy, may be required. (ClinicalTrials.gov number, NCT01683318 and NCT01448369.)

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“…Nonetheless, the classifier performance decreased, making the authors recommend their methodology only for the screening of severe cases [61]. Acharya et al focused their research on dry eye disease, first for treatment monitoring (k-NN: ACC = 99.88%; SN = 99.7%; SP = 100%) and then for diagnosis (k-NN: ACC = 99.8%; SN = 99.8%; SP = 99.8%) [62,63]. The presence of rheumatoid arthritis disease and particular areas of interest for improved diagnosis were identified by Frize et al (Decision Trees: SN = 96%; SP = 92%) [64], while Umapathy et al used k-means methods to segment hands, aiding in the evaluation of this health problem [7].…”

Section: Other Conditionsmentioning

confidence: 99%

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

2021

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Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.

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Diagnosis of response and non-response to dry eye treatment using infrared thermography images

Cited by 19 publications

References 37 publications

Automated diagnosis of dry eye using infrared thermography images

Automated diagnosis of dry eye using infrared thermography images

Longitudinal Changes in Tear Evaporation Rates After Eyelid Warming Therapies in Meibomian Gland Dysfunction

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

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