Acanthamoeba and Artificial Intelligence

Artificial Intelligence Clinical Trial

Official title:

Acanthamoeba and Artificial Intelligence: Single-center Retrospective Observational Study

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06332703
• Other study ID #: IACA
• Status: Not yet recruiting
• Start date: May 2024
• Est. completion date: April 2025

Study information

• Verified date: March 2024
• Source: IRCCS Ospedale San Raffaele
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Acanthamoeba keratitis, caused by the pathogen Acanthamoeba spp, is recognized worldwide as a severe ocular infection that can pose potential risks to vision.

This observational retrospective and single-center study, of exploratory nature, aims to determine the possibility of identifying patterns that may be useful for future rapid diagnosis of Acanthamoeba keratitis from confocal images, leveraging the normality of corneal examination and the high specificity and sensitivity of computational models.

The data will be based on patients who have been confirmed positive through laboratory tests with proven effectiveness in detecting the infection.

The laboratory tests considered for the division of patients into their respective groups are bacterial examination, PCR examination, and culture examination.

Patients were divided into two groups, the first comprising patients positive for Acanthamoeba infection, while the second comprised patients negative for Acanthamoeba but positive for other pathogens. The study will last for 18 months.

The cohort under study includes 151 patients from the IRCCS San Raffaele Hospital who underwent the aforementioned examinations, of which 76 cases will be included in the group of patients positive for Acanthamoeba and 75 in the group of controls positive for other pathogens.

The confocal images of this cohort will be fed into artificial intelligence software. To evaluate the model, the test set will be used, and the AI model's ability will be assessed using the most commonly used metrics in the field of computer vision such as accuracy, specificity, sensitivity, and f1-score; culminating in a comprehensive evaluation of the model.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 151
• Est. completion date: April 2025
• Est. primary completion date: October 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 1 Year to 99 Years

Eligibility

Inclusion Criteria:

• Performed corneal scraping and subsequent bacterioscopic exam, PCR and bacterial colture analysis between 2004 and 2023.

• Patients positivity to corneal infection.

Exclusion Criteria:

• Patients negativity to aforementioned exams.

Related Conditions & MeSH terms

• Acanthamoeba Keratitis
• Artificial Intelligence
• Keratitis

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
IRCCS Ospedale San Raffaele

References & Publications (6)

Cabrera-Aguas M, Khoo P, Watson SL. Infectious keratitis: A review. Clin Exp Ophthalmol. 2022 Jul;50(5):543-562. doi: 10.1111/ceo.14113. Epub 2022 Jun 3. — View Citation

Dart JK, Saw VP, Kilvington S. Acanthamoeba keratitis: diagnosis and treatment update 2009. Am J Ophthalmol. 2009 Oct;148(4):487-499.e2. doi: 10.1016/j.ajo.2009.06.009. Epub 2009 Aug 5. — View Citation

Lorenzo-Morales J, Khan NA, Walochnik J. An update on Acanthamoeba keratitis: diagnosis, pathogenesis and treatment. Parasite. 2015;22:10. doi: 10.1051/parasite/2015010. Epub 2015 Feb 18. — View Citation

Lv J, Zhang K, Chen Q, Chen Q, Huang W, Cui L, Li M, Li J, Chen L, Shen C, Yang Z, Bei Y, Li L, Wu X, Zeng S, Xu F, Lin H. Deep learning-based automated diagnosis of fungal keratitis with in vivo confocal microscopy images. Ann Transl Med. 2020 Jun;8(11):706. doi: 10.21037/atm.2020.03.134. — View Citation

Rampat R, Deshmukh R, Chen X, Ting DSW, Said DG, Dua HS, Ting DSJ. Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions. Asia Pac J Ophthalmol (Phila). 2021 Jul 1;10(3):268-281. doi: 10.1097/APO.0000000000000394. — View Citation

Zhang Y, Xu X, Wei Z, Cao K, Zhang Z, Liang Q. The global epidemiology and clinical diagnosis of Acanthamoeba keratitis. J Infect Public Health. 2023 Jun;16(6):841-852. doi: 10.1016/j.jiph.2023.03.020. Epub 2023 Mar 23. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Determination of the potential presence of significant patterns of Acanthamoeba infection in in-vivo confocal microscopy (IVCM) images.		IVCM and laboratory samples will be acquired at day 0 (day of enrollment).
Secondary	Correlation assessment between IVCM images and laboratory results.		IVCM and laboratory samples will be acquired at day 0 (day of enrollment).