Abstract:Objective To study the distribution and causes of false positive nodules in lung CT of physical examination by artificial
intelligence (AI). Methods A total of 500 patients who underwent lung CT examination in our hospital from August to September
in 2020 were continuously collected. All lung CT images were detected by AI software. The detected lung nodules were identified as
true-positive nodules and false-positive nodules by two radiologists, and the number, size and density of the false-positive nodules
were recorded and classified. Results A total of 1518 lung nodules were detected by AI, including 740 true-positive nodules and 778
false-positive nodules, with an average of 1.6 false-positive nodules detected by CT. Among the false-positive nodules of different
sizes, with a diameter of less than 5 mm were the most common false-positive nodules, accounting for 68.6% (534/778). Among the
false-positive nodules of different densities, the false-positive predictive value of part solid false nodules was 69.7% (23/33), which
was higher than the 48.3% (353/731) of solid false nodules and the 53.3% (402/754) of pure ground-glass false nodules,there was a
statistical difference among them (P<0.05). There was a significant positive correlation between age and the detection rate of falsepositive
nodules. Spearman rank correlation coefficient (rs)=0.986, P<0.05. The main causes of false-positive pulmonary nodules
detected by AI were pleural nodules (21.5%), cord shadows (17.9%), vascular thickening (13.8%), vascular bifurcation (12.2%) and
pulmonary lobular structure (9.0%). Conclusion The distribution and composition of false-positive pulmonary nodules detected by
AI are regular. Being familiar with these regularity can improve the doctor’s ability to identify false-positive nodules, and hope to
provide a reference for AI to reduce the false-positive rate.
左玲子,黄艳. 人工智能在体检肺CT中检出的假阳性结节研究[J]. 中国医疗设备, 2021, 36(10): 177-180.
ZUO Lingzi, HUANG Yan. Study of False Positive Nodules Detected by Artificial Intelligence in Lung CT Examination. China Medical Devices, 2021, 36(10): 177-180.