A masked study to differentiate in vivo confocal microscopic features of Pythium insidiosum and fungal filaments
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Published on: November 28, 2019
Bhupesh Bagga1, Md Hasnat Ali2, K Shreeya Jain3
1Ramoji Foundation Centre for Ocular Infection, The Shantilal Shanghvi Cornea Institute, LV Prasad Eye Institute, Hyderabad, India.
Purpose:
To describe in vivo confocal microscopic features of Pythium insidiosum in patients with Pythium keratitis and compare with those observed in fungal keratitis.
Method:
We collected in vivo confocal images of the cornea from patients with microbiologically confirmed Pythium and fungal keratitis, analysing five putative distinguishing features: filament width (broad or thin), granularity within the filament (present or absent), filament continuity or traceability, the presence or absence of loops, and the double track sign. Three masked observers were shown images with concealed identities and tasked with detecting Pythium filaments. After initial assessment and training, their detection rates were calculated and compared before and after training. We did perform imageJ (Open Source software project Fiji) analysis of all the images for objectively assessment.
Results:
Sixty confocal images of Pythium (n = 32,15 patients) and fungal (n = 28,12 patients) keratitis were analysed. The continuity of filaments and the presence of loops emerged as strong predictors of Pythium, with adjusted odds ratios (OR) of 18.1 and 19.29, respectively, based on multivariate logistic regression and decision tree splits. Pre-training accuracy was 0.51, 0.52, and 0.56, but post-training (95 % CI) improved to 0.75 (0.62-0.85), 0.80 (0.67-0.89), and 0.86 (0.75-0.94). Correct identification rates for Pythium were 27, 28, and 29 (84-89 %) out of 32, and for fungus were 16, 21, and 24 (57.4-85.7 %) out of 28 images with sensitivity and specificity ranging from 70.7 to 87.5 % and 80-85 % respectively. ImageJ analysis revealed a significant difference between Pythium and fungal filaments in both width (9.30 ± 1.21 μ vs. 6.20 ± 0.88 μ, p < 0.001) and branching angle (83.92 ± 13.57° vs. 55.10 ± 6.03°, p < 0.001).
Conclusions:
Based on our analysis, these features may be indicative of Pythium and could serve as a helpful reference for future prospective studies. However, further large scale studies and validation are needed to strengthen these observations.
Insights
In vivo confocal microscopy reveals distinct features for Pythium keratitis, differentiating it from fungal keratitis. Filament continuity and loops are key indicators, improving diagnostic accuracy after observer training.
Area of Science:
- Ophthalmology
- Medical Mycology
- Infectious Diseases
Background:
- Pythium keratitis is a severe ocular infection.
- Distinguishing Pythium keratitis from fungal keratitis is crucial for effective treatment.
- In vivo confocal microscopy offers a non-invasive method for corneal imaging.
Purpose of the Study:
- To characterize the in vivo confocal microscopic features of Pythium insidiosum in patients with Pythium keratitis.
- To compare these features with those observed in fungal keratitis.
Main Methods:
- In vivo confocal microscopy images of corneas from patients with confirmed Pythium and fungal keratitis were analyzed.
- Five features were assessed: filament width, granularity, continuity, loops, and double track sign.
- ImageJ analysis was performed for objective measurements of filament width and branching angle.
Main Results:
- Filament continuity and the presence of loops were significant predictors of Pythium keratitis (OR 18.1 and 19.29, respectively).
- Observer diagnostic accuracy improved from pre-training (51-56%) to post-training (75-86%).
- ImageJ analysis showed significant differences in filament width (9.30 µm vs. 6.20 µm) and branching angle (83.92° vs. 55.10°) between Pythium and fungal keratitis.
Conclusions:
- Specific in vivo confocal microscopic features, including filament continuity and loops, may help diagnose Pythium keratitis.
- These findings can serve as a reference for future studies.
- Larger-scale studies are required for further validation.
