AI-based radiodiagnosis using chest X-rays: A review

Abstract

Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.

Bounds for Haralick features in synthetic images with sinusoidal gradients

“…Haralick features have received much recent attention due to the advance of machine learning and artificial intelligence algorithms capable of accurately and quickly classifying textures over a wide range of applications, such as biomedical imaging (Cao et al, 2022;Devnath et al, 2022;Feng et al, 2022;Ferro et al, 2023a;Akhter et al, 2023;Ferro et al, 2023b;Criss et al, 2023;Nakata and Siina, 2023;Prinzi et al, 2023), cybersecurity (Lunt, 1993;Chang et al, 2019;Karanja et al, 2020;Baldini et al, 2021), or crowd abnormality (Sivarajasingam et al, 2003;Lloyd et al, 2017;Naik and Gopalakrishna, 2017). However, beyond five-decade-old definitions (Haralick et al, 1973), little progress has been made on the theoretical and analytical understanding of Haralick features, deriving theoretical lower and upper bounds, the dependence of GLCM and Haralick features on image depth, and other related issues.…”

“…Haralick et al (1973) and Haralick (1979) also noted that the most valuable features for texture classifications are the angular second moment, entropy, sum entropy, difference entropy, information measure of correlation, and the maximal correlation features that are due to gray-level quantization invariance. Recent advances in machine learning and AI have led to novel approaches in X-ray radiodiagnosis which are primarily focused on optimized versions of convolutional neural networks (CNNs) (Akhter et al, 2023). The first automatic system based on Haralick features used digitized lung X-rays and identified black lung disease with 96% accuracy (Kruger et al, 1974;Abe et al, 2014), while physician accuracy varies from 86% to 100% (Hall et al, 1975).…”

Performance of OpenAI’s GPT-4 in a mock MRCS Part A Examination

“…The web-based interface of ChatGPT (a free-to-use AI system) has allowed public access to GPT-4 without the need for technical knowledge and has caused a rapid exploration into potential applications of AI in clinical medicine and medical education. For instance, AI has exhibited a rudimentary capability to analyse chest radiographs [3].…”

A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography

“…Recent research has developed numerous medical image recognition algorithms for CXR patterns 4 – 6 and various pulmonary diseases (pneumonia, lung cancer, TB, pneumothorax, COVID-19, etc.) 7 – 15 , in some of them the accuracy can match or even outperform that of radiologists. Some of them had external validation confirmed accuracy 7 – 10 , 12 , 14 , 16 , 17 .…”