Clinical trial NCT06332703: Acanthamoeba and Artificial Intelligence
Official title: Acanthamoeba and Artificial Intelligence: Single-center Retrospective Observational Study
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Acanthamoeba Keratitis
Artificial Intelligence
Keywords: Acanthamoeba
Keratitis
Artificial intelligence
Corneal scraping
Confocal images
Acanthamoeba pattern
Machine learning
Lead sponsor: IRCCS Ospedale San Raffaele
Sponsor class: OTHER
First posted: 2024-03-27
Enrollment: 151
Sex: ALL
Minimum age (years): 1
Maximum age (years): 99
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Based on the results of the exams previously described in the eligibility criteria, two groups of patients will be created:

* Patients positive for Acanthamoeba infection.
* Patients negative for Acanthamoeba infection but positive for infection of another pathogen.

Confocal images of about 75 subjects from the first group and 76 from the second group, used as controls, will be entered into the artificial intelligence software.

The sample size, of this retrospective study, is based on the availability of data available in our database regarding the inclusion criteria of the study.

Brief 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.


Eligibility criteria: 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.

Primary outcomes:
measure: Determination of the potential presence of significant patterns of Acanthamoeba infection in in-vivo confocal microscopy (IVCM) images.
timeFrame: IVCM and laboratory samples will be acquired at day 0 (day of enrollment).
Secondary outcomes:
measure: Correlation assessment between IVCM images and laboratory results.
timeFrame: IVCM and laboratory samples will be acquired at day 0 (day of enrollment).
Other outcomes:
Locations:
Study officials:
References:
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.
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.
citation: 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.
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.
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.
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.