{"id":1494,"date":"2025-06-15T20:11:14","date_gmt":"2025-06-15T20:11:14","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/ai-supported-approaches-for-mammography-single-and-double-reading-a-controlled-multireader-study\/"},"modified":"2025-11-02T12:22:01","modified_gmt":"2025-11-02T12:22:01","slug":"ai-supported-approaches-for-mammography-single-and-double-reading-a-controlled-multireader-study","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/ai-supported-approaches-for-mammography-single-and-double-reading-a-controlled-multireader-study\/","title":{"rendered":"AI-supported approaches for mammography single and double reading: A controlled multireader study"},"content":{"rendered":"

AI-supported approaches for mammography single and double reading: A controlled multireader study<\/h3>\n

Beniamino Brancato , Veronica Magni, Calogero Saieva, Gabriella Gemma Risso, Francesca Buti, Sandra Catarzi, Fiorella Ciuffi, Francesca Peruzzi, Francesco Regini, Daniela Ambrogetti, Giuseppe Alabiso, Anna Cruciani, Valeria Doronzio, Sara Frati, Gian Piero Giannetti, Claudio Guerra, Pietro Valente, Chiara Vignoli, Stefano Atzori, Valentina Carrera, Andrea Cozzi<\/p>\n

European Journal of Radiology, 2025<\/strong><\/p>\n

Abstract<\/strong>
\nPurpose<\/strong>
\nTo assess the impact of artificial intelligence (AI) on the diagnostic performance of radiologists with varying experience levels in mammography reading, considering single and simulated double reading approaches.<\/p>\n

Methods<\/strong>
\nIn this retrospective study, 150 mammography examinations (30 with pathology-confirmed malignancies, 120 without malignancies [confirmed by 2-year follow-up]) were reviewed according to five approaches: A) human single reading by 26 radiologists of varying experience; B) AI single reading (Lunit INSIGHT MMG; C) human single reading with simultaneous AI support; D) simulated human\u2013human double reading; E) simulated human-AI double reading, with AI as second independent reader flagging cases with a cancer probability \u226510 %. Sensitivity and specificity were calculated and compared using McNemar\u2019s test, univariate and multivariable logistic regression.<\/p>\n

Results<\/strong>
\nCompared to single reading without AI support, single reading with simultaneous AI support improved mean sensitivity from 69.2 % (standard deviation [SD] 15.6) to 84.5 % (SD 8.1, p < 0.001), providing comparable mean specificity (91.8 % versus 90.8 %, p = 0.06). The sensitivity increase provided by the AI-supported single reading was largest in the group of radiologists with a sensitivity below the median in the non-supported single reading, from 56.7 % (SD 12.1) to 79.7 % (SD 10.2, p < 0.001). In the simulated human-AI double reading approach, sensitivity further increased to 91.8 % (SD 3.4), surpassing that of the human\u2013human simulated double reading (87.4 %, SD 8.8, p = 0.016), with comparable mean specificity (from 84.0 % to 83.0 %, p = 0.17).<\/p>\n

Conclusion<\/strong>
\nAI support significantly enhanced sensitivity across all reading approaches, particularly benefiting worse performing radiologists. In the simulated double reading approaches, AI incorporation as independent second reader significantly increased sensitivity without compromising specificity.<\/p>\n

Read the full paper<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[],"publication-region":[89],"publication-type":[],"radiology":[97,102,96],"class_list":["post-1494","publication","type-publication","status-publish","hentry","publication-region-europe","radiology-breast","radiology-different-workflows-with-ai","radiology-lunit-insight"],"acf":[],"yoast_head":"\nAI-supported approaches for mammography single and double reading: A controlled multireader study - Lunit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lunit.io\/en\/publication\/ai-supported-approaches-for-mammography-single-and-double-reading-a-controlled-multireader-study\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI-supported approaches for mammography single and double reading: A controlled multireader study - 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