Bone　Age　(BA)　is　reckoned　to　be　closely　associated　with　the　growth　and　development　of　teenagers,　whose　assessment　highly　depends　on　the　accurate　extraction　of　the　reference　bone　from　the　carpal　bone.　Being　uncertain　in　its　proportion　and　irregular　in　its　shape,　wrong　judgment　and　poor　average　extraction　accuracy　of　the　reference　bone　will　no　doubt　lower　the　accuracy　of　Bone　Age　Assessment　(BAA).　In　recent　years,　machine　learning　and　data　mining　are　widely　embraced　in　smart　healthcare　systems.　Using　these　two　instruments,　this　article　aims　to　tackle　the　aforementioned　problems　by　proposing　a　Region　of　Interest　(ROI)　extraction　method　for　wrist　X-ray　images　based　on　optimized　YOLO　model.　The　method　combines　Deformable　convolution-focus　(Dc-focus),　Coordinate　attention　(Ca)　module,　Feature　level　expansion,　and　Efficient　Intersection　over　Union　(EIoU)　loss　all　together　as　YOLO-DCFE.　With　the　improvement,　the　model　can　better　extract　the　features　of　irregular　reference　bone　and　reduce　the　potential　misdiscrimination　between　the　reference　bone　and　other　similarly　shaped　reference　bones,　improving　the　detection　accuracy.　We　select　10041　images　taken　by　professional　medical　cameras　as　the　dataset　to　test　the　performance　of　YOLO-DCFE.　Statistics　show　the　advantages　of　YOLO-DCFE　in　detection　speed　and　high　accuracy.　The　detection　accuracy　of　all　ROIs　is　99.8%,　which　is　higher　than　other　models.　Meanwhile,　YOLO-DCFE　is　the　fastest　of　all　comparison　models,　with　the　Frames　Per　Second　(FPS)　reaching　16.