Deep learning — a form of artificial intelligence — was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists.

Deep learning system predicts lung cancer. (Credit: Northwestern)

This deep-learning system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment. The deep-learning system was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists.

The system also produced fewer false positives and fewer false negatives, which could lead to fewer unnecessary follow-up procedures and fewer missed tumors, if it were used in a clinical setting. The system utilizes both the primary CT scan and, whenever available, a prior CT scan from the patient as input. The novel system identifies both a region of interest and whether the region has a high likelihood of lung cancer.

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Topics:
AR/AI Anatomy Artificial Intelligence Artificial intelligence (AI) Diagnosis Diseases Imaging Imaging Imaging and visualization Medical Medical, health and wellness Medical, health, and wellness Optics Respiratory system

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    This article first appeared in the July, 2019 issue of Medical Design Briefs Magazine (Vol. 9 No. 7).

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