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Artificial Intelligence (AI) might enable non-invasive physiology from invasive coronary angiography (CAG), possibly outperforming humans, but has seldom been explored, especially for instantaneous wave-free Ratio (iFR). We aimed to develop binary iFR lesion classification AI models and compare them with human performance. single-center retrospective study of patients undergoing CAG and iFR. A validated encoder-decoder convolutional neural network (CNN) performed segmentation. Manual annotation of target vessel and pressure sensor location on a segmented telediastolic frame followed. Three AI models classified lesions as positive (\u2264\u20090.89) or negative (&gt;\u20090.89). Model 1 uses preprocessed vessel diameters with a transformer. Models 2\/3 are EfficientNet-B5 CNNs using concatenated angiography and segmentation - Model 3 employs class-frequency-weighted Cross-Entropy Loss. Previous findings demonstrated Model 3\u2019s superiority for left anterior descending (LAD) and Model 1\u2019s for circumflex (Cx)\/right coronary artery (RCA) - they were therefore unified into a vessel-based model. Ten-fold patient-level cross-validation enabled full sample training\/testing. Three experienced operators performed binary iFR classification using single frames of raw\/segmented images. Comparison metrics were accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Across 250 measurements, AI accuracy was 72%, PPV 48%, NPV 90%, sensitivity 77%, and specificity 71%. Human accuracy ranged from 54 to 74%. NPV was high for the Cx\/RCA (AI: 96\/98%; operators: 94\/97%), but AI significantly outperformed humans in the LAD (78% vs. 60\u201364%). An AI model capable of binary iFR lesions classification mildly outperformed interventional cardiologists, supporting further validation studies.<\/jats:p>","DOI":"10.1007\/s10554-025-03369-y","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:37:08Z","timestamp":1741567028000},"page":"755-771","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Non-invasive derivation of instantaneous free-wave ratio from invasive coronary angiography using a new deep learning artificial intelligence model and comparison with human operators\u2019 performance"],"prefix":"10.1007","volume":"41","author":[{"given":"Catarina","family":"Oliveira","sequence":"first","affiliation":[]},{"given":"Marta","family":"Vilela","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Silva Marques","sequence":"additional","affiliation":[]},{"given":"Cl\u00e1udia","family":"Jorge","sequence":"additional","affiliation":[]},{"given":"Tiago","family":"Rodrigues","sequence":"additional","affiliation":[]},{"given":"Ana Rita","family":"Francisco","sequence":"additional","affiliation":[]},{"given":"Rita Marante de","family":"Oliveira","sequence":"additional","affiliation":[]},{"given":"Beatriz","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o Louren\u00e7o","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Arlindo L.","family":"Oliveira","sequence":"additional","affiliation":[]},{"given":"Fausto J.","family":"Pinto","sequence":"additional","affiliation":[]},{"given":"Miguel","family":"Nobre Menezes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"3369_CR1","doi-asserted-by":"publisher","first-page":"203","DOI":"10.4244\/EIJ-D-22-00969","volume":"19","author":"D Faria","year":"2023","unstructured":"Faria D, Hennessey B, Shabbir A, Mej\u00eda-Renteria H, Wang L, Myung Lee J et al (2023) Functional coronary angiography for the assessment of the epicardial vessels and the microcirculation. 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