Validation of an Artificial Intelligence model for reading chest X-rays: protocol of a prospective study (Preprint)

Abstract

BACKGROUND Chest X-rays are the most commonly used type of X-rays today, accounting for up to 26% of all radiographic tests performed. However, chest radiography is a complex imaging modality to interpret; several studies have reported discrepancies in chest X-ray interpretations among emergency physicians and radiologists. It is of vital importance to be able to offer a fast and reliable diagnosis for this kind of X-ray, using artificial intelligence (AI) to support the clinician. Oxipit has developed an AI algorithm for reading chest X-rays, available through a web platform called ChestEye. This platform is an automatic computer-aided diagnosis (CAD) system where a reading of the inserted chest X-ray is performed and an automatic report is returned with a capacity to detect 75 pathologies, covering 90% of diagnoses. OBJECTIVE The overall objective of the study is to perform a validation with prospective data of the ChestEye algorithm as a diagnostic aid. We wish to validate the algorithm for a single pathology and multiple pathologies by evaluating the accuracy, sensitivity and specificity of the algorithm. METHODS A prospective study will be carried out to compare the diagnosis of the reference radiologist for the users attending the primary care centre in the Osona region (Spain), with the diagnosis of the ChestEye AI algorithm. RESULTS Patient recruitment began in February 2022 on a rolling basis until the target sample was reached. It is hoped to obtain sufficient evidence to demonstrate that the use of AI in the reading of chest X-rays can be a good tool for diagnostic support. However, there is a decreasing number of radiology professionals and, therefore, it is necessary to develop and validate tools to support professionals who have to interpret these tests. CONCLUSIONS If the results of the validation of the model are satisfactory, it could be implemented as a support tool and allow an increase in the accuracy and speed of diagnosis, patient safety and agility in the primary care system, and reduce the costs of unnecessary tests. CLINICALTRIAL 21/288

A novel two-stage deep learning model used to assist in diagnosing neonatal necrotizing enterocolitis and determining the need for surgical treatment

“…In recent years, computer vision and its related fields have been developing very rapidly and are playing an increasingly important role in aiding disease diagnosis, including colorectal Cancer [17,18] , thyroid nodules [19,20] , congenital heart disease [21,22] , Alzheimer's disease [23,24] , breast cancer [25][26][27] , dermatology [28][29][30] and screening of abnormality [31][32][33] . Progress has been made in applying deep learning to different medical application tasks, such as image classification [19,34,35] , semantic segmentation [36][37][38] , object detection [39][40][41][42] , instance segmentation [43,44] .…”