How AI Can Tackle Challenges in Tumour Grading in Lung Adenocarcinoma
A ground breaking technology utilizing artificial intelligence (AI) to diagnose a common lung condition more quickly and accurately is set to be introduced in GP surgeries, according to its developers.
The device, which has been described as “revolutionary” by medical professionals, employs carbon dioxide sensors to monitor changes in lung function.
Known as N-Tidal Diagnose, the AI-powered device can deliver a diagnosis within five minutes
The diagnostic test has received regulatory approval and is CE-marked under EU Medical Device Regulations for identifying COPD, a group of lung conditions, including emphysema and chronic bronchitis, that result in breathing difficulties.
Approximately 1.2 million individuals in the UK are diagnosed with COPD, while an estimated two million remain undiagnosed.
The current diagnostic procedure, spirometry testing, measures the volume of air a patient can inhale and exhale, with appointments typically lasting 30 to 90 minutes.
Dr. Ameera Patel, CEO of TidalSense, believes the test could support the Government in achieving its objective of reducing NHS waiting times.
Dr. Patel stated: “For years, respiratory diagnostics have lagged behind advancements in other areas of medicine, despite lung disease being one of the most significant healthcare challenges in the UK.
“The N-Tidal Diagnose device represents a major breakthrough in COPD diagnosis, addressing both patient needs and systemic healthcare demands, and is designed for extensive deployment across the NHS.”
Challenges in Tumour Grading in Lung Adenocarcinoma
Lung adenocarcinoma typically exhibits one of six growth patterns, though individual tumours often contain multiple patterns.
A global grading framework, the International Association for the Study of Lung Cancer (IASLC) system, suggests these patterns help predict the risk of disease progression or recurrence in patients.
The presence of diverse pattern types within a tumour complicates pathologists’ ability to assess a patient’s prognosis.
Moreover, the appearance of each pattern type can vary widely, making it challenging to define and quantify these patterns.
Consequently, different pathologists may arrive at differing conclusions when grading a tumour.
Inconsistent or inaccurate tumour grading can result in unsuitable or inadequate treatments for some patients, potentially leading to worse health outcomes.
