Objective:　We　aimed　to　establish　an　artificial　intelligence　(AI)　model　to　identify　parathyroid　glands　during　endoscopic　approaches　and　compare　it　with　senior　and　junior　surgeons＇　visual　estimation.　Methods:　A　total　of　1,700　images　of　parathyroid　glands　from　166　endoscopic　thyroidectomy　videos　were　labeled.　Data　from　20　additional　full-length　videos　were　used　as　an　independent　external　cohort.　The　YOLO　V3,　Faster　R-CNN,　and　Cascade　algorithms　were　used　for　deep　learning,　and　the　optimal　algorithm　was　selected　for　independent　external　cohort　analysis.　Finally,　the　identification　rate,　initial　recognition　time,　and　tracking　periods　of　PTAIR　(Artificial　Intelligence　model　for　Parathyroid　gland　Recognition),　junior　surgeons,　and　senior　surgeons　were　compared.　Results:　The　Faster　R-CNN　algorithm　showed　the　best　balance　after　optimizing　the　hyperparameters　of　each　algorithm　and　was　updated　as　PTAIR.　The　precision,　recall　rate,　and　F1　score　of　the　PTAIR　were　88.7%,　92.3%,　and　90.5%,　respectively.　In　the　independent　external　cohort,　the　parathyroid　identification　rates　of　PTAIR,　senior　surgeons,　and　junior　surgeons　were　96.9%,　87.5%,　and　71.9%,　respectively.　In　addition,　PTAIR　recognized　parathyroid　glands　3.83　s　ahead　of　the　senior　surgeons　(p = 0.008),　with　a　tracking　period　62.82　s　longer　than　the　senior　surgeons　(p = 0.006).　Conclusions:　PTAIR　can　achieve　earlier　identification　and　full-time　tracing　under　a　particular　training　strategy.　The　identification　rate　of　PTAIR　is　higher　than　that　of　junior　surgeons　and　similar　to　that　of　senior　surgeons.　Such　systems　may　have　utility　in　improving　surgical　outcomes　and　also　in　accelerating　the　education　of　junior　surgeons.　Level　of　Evidence:　3　Laryngoscope,　132:2516–2523,　2022.　©　2022　The　American　Laryngological,　Rhinological　and　Otological　Society,　Inc.