Acanthamoeba are ubiquitous microorganisms found in the air, soil, tap and drinking water, swimming pools, and in both saltwater and freshwater environments [1].They can cause corneal infections that lead to Acanthamoeba keratitis (AK), which is serious medical condition accompanied by intense eye pain, sensitivity to light, and severely impaired vision. AK is commonly associated with contact lens use, particularly in developed countries, where up to 90% of cases are linked to contact lens wear [2]. Timely identification and diagnosis of AK are critical to preventing vision loss. Corneal culture is the gold standard for diagnosing AK, but its low sensitivity and slow turnaround can delay treatment and worsen infection [3]. A complementary method uses non-invasive clinical imaging such as in-vivo confocal microscopy (IVCM) [4,5]. Despite several advantages such as high-resolution imaging, rapid diagnosis, and the ability to monitor disease progression, IVCM has limitations, including the large volume of images generated and the needs for identifying informative/useful images and experienced interpretation. While previous approaches have primarily focused on building models to detect AK in pre- selected images, our decision support system [6], IVCMAssist, automates the entire diagnostic workflow—from processing raw IVCM images and removing artifacts, to sorting images by corneal layer, to identifying informative images, and to detecting AK signs such as cysts in informative images. It enhances the AK diagnosis process by reducing the time spent on image sorting and pre-selection of informative images (non-overlapped images with detected AK cysts), while enabling experienced observers to correct model errors through a human-in-the-loop AI approach.