An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study

作者：Inge A. H. van den Berk, Colin Jacobs, Maadrika M. N. P. Kanglie, Onno M. Mets, Miranda Snoeren, Alexander D. Montauban van Swijndregt, Elisabeth M. Taal, Tjitske S. R. van Engelen, Jan M. Prins, Shandra Bipat, Patrick M. M. Bossuyt, Jaap Stoker, The OPTIMACT study group, Jouke T. Annema, Ludo F.M. Beenen, Dominique Bekebrede-Kaufman, Inge A. H. van den Berk, Patrick M. M. Bossuyt, Brenda Elzer, Tjitske S. R. van Engelen, Betty Frankemölle, Maarten Groenink, Erwin Hoolwerf, Dorine Hulzebosch, Maadrika M. N. P. Kanglie, Saskia Kolkman, N. H. J. Lobé, Peter A. Leenhouts, Onno M. Mets, Mélanie A. Monraats, Jan S. K. Luitse, Saskia Middeldorp, Alexander Montauban van Swijndregt, Jacqueline Otker, Adriënne van Randen, Milan L. Ridderikhof, Johannes A. Romijn, Maeke J. Scheerder, Antoinet J. N. Schoonderwoerd, Laura J. Schijf, Frank F. Smithuis, Jaap Stoker, Geert J. Streekstra, Elizabeth M. Taal, Glenn de Vries, Maaike J. A. Vogel, Ibtisam Yahya · 发表于：European Radiology Experimental · 年份：2024 · DOI：10.1186/s41747-024-00518-1 · 被引用次数：7 · 研究领域：Lung Cancer Diagnosis and Treatment、Ultrasound in Clinical Applications、COVID-19 diagnosis using AI

BACKGROUND: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED). METHODS: In the OPTIMACT trial, 870 patients with suspected nontraumatic pulmonary disease underwent ULDCT. The ED radiologist prospectively read the examinations and reported incidental pulmonary nodules requiring follow-up. All ULDCTs were processed post hoc using an AI deep learning software marking pulmonary nodules ≥ 6 mm. Three chest radiologists independently reviewed the subset of ULDCTs with either prospectively detected incidental nodules in 35/870 patients or AI marks in 458/870 patients; findings scored as nodules by at least two chest radiologists were used as true positive reference standard. Proportions of true and false positives were compared. RESULTS: During the OPTIMACT study, 59 incidental pulmonary nodules requiring follow-up were prospectively reported. In the current analysis, 18/59 (30.5%) nodules were scored as true positive while 104/1,862 (5.6%) AI marks in 84/870 patients (9.7%) were scored as true positive. Overall, 5.8 times more (104 versus 18) true positive pulmonary nodules were detected with the use of AI, at the expense of 42.9 times more (1,758 versus 41) false positives. There was a median number of 1 (IQR: 0-2) AI mark per ULDCT. CONCLUSION: The use of AI on ULDCT in patients suspected of pulmonary disease in an emergency...