Modular Diagnosis of Cervical Cancer Utilizing Smartphone Diagnostics and Artificial Intelligence
L Auguste, D Palsana, S Kochar, D Luci, S Malav
With the support of the United States and India Science and Technology Endowment Foundation (USISTEF) and Qualcomm Design in India (QDIC), engineers from Alexapath developed a portable microscope system capable of taking glass Pap Smear slides as an input and able to provide Computer Assisted Diagnosis (CAD). The CAD solution called AutoPap is able to analyze the digitized mobile Whole Slide Image (mWSI)1 and determine whether or not there are abnormal cells present. The microscope system - AiDA (Artificial intelligence Diagnostic Assistant) easily fits on any counter top and requires no external wifi or expensive GPUs. The artificial intelligence is able to run on any device with a processor equal to or better than the SnapDragon 820 chip. In order to succeed, the following four tasks were completed: 1- build a robotic microscope capable of imaging glass slides, 2- develop software for acquisition and viewing of digitized glass slides, 3- create a Convolutional Neural Network (CNN) capable of classification and 4- deploy the software and Ai onto a smartphone. The end to end solution including a microscope, camera, and smartphone is both portable and affordable. The potential use for the system is akin to triage. With such a large number of women in India needing cervical cancer screenings, a solution that can identify the presence of abnormal cells in pap smears and has a turn around time of less than six minutes can prove a powerful ally - allowing human cytologists to only focus on the small number of cases needing staging. Additionally since AutoPap functions as an “Assisted” Diagnosis, which does not require De Novo classification, AutoPap maybe able to quickly enter the market. The work contained in this paper is the work of Alexapath LLC with funding from the USISTEF and QDIC. There are no conflicts of interest to report.
Mobile Whole Slide Imaging (mWSI):
a low resource acquisition and transport technique for microscopic pathological specimens

Louis Auguste, Dhaval Palsana
The Open Mobile Telepathology System (OMT) is a combination of two components, the Pocket Electronic Health Record (pEHR) and the Mobile Whole Slide Imaging (mWSI) app. This system was created over the course of this study to help reduce the cost of telepathology in developing countries. The affordable system as described will help to expedite the diagnostic process in low-resource environments and provide more patients with better health outcomes. The OMT system offers a number of advantages to the standard Whole Slide Imaging machines. It can be deployed at a fraction of the cost, the images are more easily transportable at an average image size of less than 500 MBs and any worker, even someone without any knowledge of pathology, can perform the scans; also, components can be replaced and upgraded at a minimal cost, offering a large advantage to low resource and rural environments. The OMT system utilises a standard light microscope with a custom-built 3D adaptor and the iPhone 5s. Acquired mWSIs can be transported through the cloud using the pEHR database and accessed through web and mobile platforms. Therefore it can be used in any part of the world with an Internet connection. The study follows the step-by-step process used to acquire mWSIs of various stains including H&E stains, and thin preps. The results of the tests have been found to be of a diagnostic quality and the imaging process as described has been optimised and standardised over the course of the 2-year study.