Researchers from Osaka Metropolitan University have developed an AI model that diagnoses achalasia using plain chest X-rays, outperforming physicians in sensitivity and specificity, and potentially transforming routine screening.

Since chest X-rays are routinely performed during health checkups in Japan, this AI-based method could enable widespread, minimally invasive screening for achalasia, making early detection more accessible.

Early diagnosis of achalasia is critical because delays averaging 6.5 years from symptom onset can lead to worsened esophageal dilation and reduced treatment success, highlighting the importance of effective screening tools.

The study, published in Clinical Gastroenterology and Hepatology, underscores the clinical significance of AI in improving diagnostic processes for esophageal disorders like achalasia.

Heatmap visualizations generated by the AI system enhance interpretability by highlighting regions of dilation and structural abnormalities in the esophagus, increasing clinical trust in the technology.

While traditional diagnosis relies on invasive procedures such as endoscopy and manometry, AI can identify features like esophageal dilation and twisting on X-rays, offering a less invasive alternative.

Although promising, further multicenter trials are needed to validate the AI's effectiveness across diverse populations, with future integration of additional data types expected to improve accuracy.

The AI system is designed as an aid for physicians, serving as a screening tool to guide further testing and facilitate earlier diagnosis and management of achalasia.

This development illustrates how existing imaging modalities, enhanced by AI, can reveal diagnostic information previously invisible, transforming routine radiography into a powerful diagnostic tool.

Implementing this AI tool could significantly reduce diagnostic delays, allowing for earlier intervention and improved patient outcomes.

Achalasia, characterized by impaired esophageal movement and symptoms like food retention and chest pain, is often diagnosed with vague signs on X-ray, but AI can help identify these signs more reliably.