AI Breakthrough: 97% Accuracy in Lung Disease Detection and COVID-19 Differentiation

AI breakthrough detects lung diseases with 97% accuracy and differentiates pneumonia from COVID-19

A new study led by researchers at Charles Darwin University (CDU), United International University and the Australian Catholic University (ACU) reveals an AI model capable of detecting lung diseases with a remarkable 97% accuracy, using ultrasound videos. Not only does it pinpoint conditions like pneumonia and COVID-19, but it also explains its decisions, offering vital support to doctors for faster, more accurate diagnoses. Combining two advanced AI techniques, the system excels at detecting the smallest details in ultrasound frames -- details that are often invisible to the human eye. The model is already outperforming older AI tools and is even capable of distinguishing between pneumonia and COVID-19, which can be tough for radiologists to differentiate in ultrasound scans. The tool is set to be a game-changer in healthcare, offering a powerful aid for medical professionals worldwide.

New AI picks up 97% of lung diseases, and can tell pneumonia from COVID-19

While they look different, on frames of ultrasounds they can be harder for the naked eye to distinguish A breakthrough new AI model is able to detect the presence of different lung diseases from ultrasound videos, with 96.57% accuracy, and it is even able to distinguish whether the abnormalities are due to pneumonia, COVID-19 or other conditions. The model, developed by researchers at Australia's Charles Darwin University (CDU), United International University and the Australian Catholic University (ACU), can identify specific patterns of different lung disease, outperforming previous AI tools that have been tested on the same ultrasound datasets. "The model also uses AI techniques to show radiologists why it made certain decisions, making it easier for them to trust and understand the results," said study co-author Niusha Shafiabady, a professor at CDU. "This model helps doctors diagnose lung diseases quickly and accurately, supports their decision-making, saves time, and serves as a valuable training tool." The team combined two kinds of AI models, highlighting how adaptable the technology is for diagnostic needs. One, known as a convolutional neural network (CNN), seeks out patterns in images or frames, focusing on the tiniest pixel-based changes that the human eye can miss when examining scans. Then, a long short-term memory (LSTM) model uses this information and puts it into a broader context, analyzing the CNN's data over time while 'forgetting' irrelevant data. With their powers combined, the novel hybrid model known as TD-CNNLSTM-LungNet is able to pick up abnormalities incredibly well - and then explain what the issue is. What's more, it can determine if the scans show evidence of pneumonia, COVID-19, other lung diseases or if the lungs are normal. With a high 'recall' rate of 96.51%, this essentially means there are very few false negatives identified by the AI - which is important in treating time-critical lung conditions. Using ultrasound videos of existing datasets, the model surpassed existing AI diagnostic tools, which currently score around 90-92%. While there's little doubt that AI diagnostics will soon be commonplace in clinics, skepticism and distrust of this emerging technology remains. While the AI chatbots we can interact with now are not trained to clinically assess medical scans or tests at this stage, specific models are being developed across the board to be reliable tools in healthcare. For example, just a year ago the Food and Drug Administration (FDA) approved the use of the DermaSensor device, the first smartphone AI-powered device that was shown to be able to detect around 200 different kinds of skin cancers. (Incidentally, I had a basal cell carcinoma identified through a similar smartphone camera device and AI software.) While the tools are not intended to replace medical professionals - my skin-cancer specialist correctly identified the cancerous spot on my back before using the AI camera device - they are set to become the most beneficial and affordable technology we've ever seen in medicine. This new lung disease AI model gives us a clue as to what is yet to come, with it able to correctly identify nuances that differentiate, for example COVID-19 from pneumonia. As the researchers noted, both of those conditions appeared similar to the human eye, but had distinct patterns that enabled the AI model to spot the difference. It would then produce a report as to why it came to its conclusion for each scan. "The explainability of the proposed model aims to increase the reliability of this approach," said Shafiabady. "The system shows doctors why it made certain decisions using visuals like heat maps. This interpretation technique will aid a radiologist in localizing the focus area and improve clinical transparency substantially." In 2024, huge strides were made by Google in the area of medical diagnostics and AI. Similarly, the technology is being developed for healthcare to assist with everything from surgery to drug discovery. It's already shown its potential for use in detecting brain tumors and other cancers. Shafiabady noted that as long as the model is trained on the right data, it has the potential to further its lung-disease diagnostic abilities, picking up signs of tuberculosis, black lung, asthma, cancer, chronic lung disease and pulmonary fibrosis. And the researchers hope to adapt the model to be able to accurately assess more than ultrasounds, such as CT scans and X-rays.

Researchers train AI to diagnose lung diseases with 96.57% accuracy

Artificial Intelligence (AI) could become a radiologist's best friend, with researchers training the technology to accurately diagnose pneumonia, COVID-19 and other lung diseases. The new study by researchers from Charles Darwin University (CDU), United International University, and Australian Catholic University (ACU) describes developing and training an AI model to analyze lung ultrasound videos and diagnose respiratory diseases. The study is published in the journal Frontiers in Computer Science. The model works by examining each video frame to find important features of the lungs and assessing the order of the video frames to understand the patterns of the lungs over time. The model then identifies specific patterns indicating different lung diseases and, based on this information, classifies the ultrasound into a diagnosis category such as normal, pneumonia, COVID-19 and other lung diseases. Co-author and CDU adjunct Associate Professor Niusha Shafiabady said the model had an accuracy of 96.57%, with the AI analyses verified by medical professionals. "The model also uses AI techniques to show radiologists why it made certain decisions, making it easier for them to trust and understand the results," Associate Professor Shafiabady said. The model uses explainable AI, a method which allows human users to understand and trust the results created by machine learning algorithms. "The explainability of the proposed model aims to increase the reliability of this approach," Associate Professor Shafiabady said. "The system shows doctors why it made certain decisions using visuals like heatmaps. This interpretation technique will aid a radiologist in localizing the focus area and improve clinical transparency substantially. "This model helps doctors diagnose lung diseases quickly and accurately, supports their decision-making, saves time, and serves as a valuable training tool." Associate Professor Shafiabady said if fed the appropriate data, the model could be trained to identify more diseases such as tuberculosis, black lung, asthma, cancer, chronic lung disease, and pulmonary fibrosis. The study was led by researchers at United International University in Bangladesh, alongside CDU researchers Dr. Asif Karim, Dr. Sami Azam, Dr. Kheng Cher Yeo, Professor Friso De Boer and Associate Professor Niusha Shafiabady, who is also a researcher at ACU. Potential avenues for research include training the model to assess other imaging, such as CT scans and X-rays.