Application of artificial intelligence technologies in detecting adrenal neoplasms on computed tomography scans

BACKGROUND: Adrenal neoplasms are a common incidental finding on computed tomography, which remains the primary imaging method used to make a presumptive diagnosis of the lesion’s nosological type. Artificial intelligence-based software solutions for detecting adrenal neoplasms on computed tomography scans have been actively developed and implemented. AIM: This study aimed to assess the diagnostic effectiveness of artificial intelligence-based software in identifying adrenal neoplasms on computed tomography images of the chest and abdominal organs available as of the first quarter of 2024. METHODS: Artificial intelligence-based software was tested in two modifications: a single-purpose service designed to detect adrenal neoplasms and comprehensive artificial intelligence service for analyzing non-contrast computed tomography image series of the chest and abdominal organs (including contrast-enhanced studies). Two datasets were used: dataset 1 included abdominal computed tomography scans, and dataset 2 comprised chest computed tomography scans. Each dataset consisted of 100 anonymized computed tomography studies of patients with (n = 50) and without (n = 50) adrenal neoplasms. The diagnostic accuracy of artificial intelligence-based software was determined by calculating the following statistical metrics: area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS: Testing of the artificial intelligence-based software on datasets with signs of adrenal neoplasms demonstrated high diagnostic accuracy metrics that exceeded the declared performance values. AUC ranged from 0.858 to 0.995 (the highest value was achieved by the artificial intelligence single-purpose service-2 for analyzing abdominal computed tomography images); specificity ranged from 0.920 to 1.000 (the highest value was achieved by the artificial intelligence comprehensive service-2 for analyzing chest computed tomography images); and sensitivity ranged from 0.739 to 1.000 (the highest values were achieved by the artificial intelligence single-purpose and artificial intelligence comprehensive service-2 for analyzing abdominal computed tomography images). CONCLUSION: Artificial intelligence-based software for detecting adrenal neoplasms showed high diagnostic accuracy across all the evaluated metrics. Therefore, such systems may be effective for identifying adrenal neoplasms on chest and abdominal computed tomography scans of the chest and abdominal organs.