Background:　The　preservation　of　parathyroid　glands　is　crucial　in　endoscopic　thyroid　surgery　to　prevent　hypocalcemia　and　related　complications.　However,　current　methods　for　identifying　and　protecting　these　glands　have　limitations.　We　propose　a　novel　technique　that　has　the　potential　to　improve　the　safety　and　efficacy　of　endoscopic　thyroid　surgery.　Purpose:　Our　study　aims　to　develop　a　deep　learning　model　called　PTAIR　2.0　(Parathyroid　gland　Artificial　Intelligence　Recognition)　to　enhance　parathyroid　gland　recognition　during　endoscopic　thyroidectomy.　We　compare　its　performance　against　traditional　surgeon-based　identification　methods.　Materials　and　methods:　Parathyroid　tissues　were　annotated　in　32　428　images　extracted　from　838　endoscopic　thyroidectomy　videos,　forming　the　internal　training　cohort.　An　external　validation　cohort　comprised　54　full-length　videos.　Six　candidate　algorithms　were　evaluated　to　select　the　optimal　one.　We　assessed　the　model＇s　performance　in　terms　of　initial　recognition　time,　identification　duration,　and　recognition　rate　and　compared　it　with　the　performance　of　surgeons.　Results:　Utilizing　the　YOLOX　algorithm,　we　developed　PTAIR　2.0,　which　demonstrated　superior　performance　with　an　AP50　score　of　92.1%.　The　YOLOX　algorithm　achieved　a　frame　rate　of　25.14　Hz,　meeting　real-time　requirements.　In　the　internal　training　cohort,　PTAIR　2.0　achieved　AP50　values　of　94.1%,　98.9%,　and　92.1%　for　parathyroid　gland　early　prediction,　identification,　and　ischemia　alert,　respectively.　Additionally,　in　the　external　validation　cohort,　PTAIR　outperformed　both　junior　and　senior　surgeons　in　identifying　and　tracking　parathyroid　glands　(p　＜　0.001).　Conclusion:　The　AI-driven　PTAIR　2.0　model　significantly　outperforms　both　senior　and　junior　surgeons　in　parathyroid　gland　identification　and　ischemia　alert　during　endoscopic　thyroid　surgery,　offering　potential　for　enhanced　surgical　precision　and　patient　outcomes.