The　underuse　of　invasive　fractional　flow　reserve　(FFR)　in　clinical　practice　has　motivated　research　towards　non-invasive　prediction　of　FFR.　Although　the　non-invasive　derivation　of　FFR　(FFRCT)　using　computational　fluid　dynamics　(CFD)　principles　has　become　a　common　practice,　its　clinical　application　has　been　limited　due　to　the　considerable　time　required　for　computation　of　resulting　changes　in　haemodynamic　conditions.　An　alternative　to　CFD　technology　is　incorporating　a　neural　network　into　the　computational　process　to　reduce　the　time　necessary　for　running　an　effective　model.　In　this　study　we　propose　a　cascade　of　data-driven　and　physic-based　neural　networks　(DP-NN)　for　predicting　FFR　(DL-FFRCT).　The　first　network　of　cascade　network　DP-NN　includes　geometric　features,　and　the　second　network　includes　physical　features.　We　compare　the　differences　between　data-driven　neural　network　(D-NN)　and　DP-NN　for　predicting　FFR.　The　training　and　testing　datasets　were　obtained　by　solving　the　three-dimensional　incompressible　Navier–Stokes　equations.　Coronary　flow　and　geometric　features　were　used　as　inputs　to　train　D-NN.　In　DP-NN　the　training　process　involves　first　training　a　D-NN　to　output　resting　ΔP　as　one　input　feature　to　the　DP-NN.　Secondly,　the　physics-based　microcirculatory　resistance　as　another　input　feature　to　the　DP-NN.　Using　clinically　measured　FFR　as　the　“gold　standard＂,　we　validated　the　computational　accuracy　of　DL-FFRCT　in　77　patients.　Compared　to　D-NN,　DP-NN　improved　the　prediction　of　ΔP　(R2　=　0.87　vs.　R2　=　0.92).　Statistical　analysis　demonstrated　that　the　diagnostic　accuracy　of　DL-FFRCT　was　not　inferior　to　FFRCT　(85.71　%　vs.　88.3　%)　and　the　computational　time　was　reduced　by　a　factor　of　approximately　3000　(4.26　s　vs.　3.5　h).　DP-NN　represents　a　near　real-time,　interpretable,　and　highly　accurate　deep-learning　network,　which　contributes　to　the　development　of　high-performance　computational　methods　for　haemodynamics.　We　anticipate　that　DP-NN　will　enable　near　real-time　prediction　of　DL-FFRCT　in　personalized　narrow　blood　vessels　and　provide　guidance　for　cardiovascular　disease　treatments.　©　2024　Elsevier　Ltd