PurposeThe　diagnosis　of　obstructive　sleep　apnea　(OSA)　relies　on　time-consuming　and　complicated　procedures　which　are　not　always　readily　available　and　may　delay　diagnosis.　With　the　widespread　use　of　artificial　intelligence,　we　presumed　that　the　combination　of　simple　clinical　information　and　imaging　recognition　based　on　facial　photos　may　be　a　useful　tool　to　screen　for　OSA.MethodsWe　recruited　consecutive　subjects　suspected　of　OSA　who　had　received　sleep　examination　and　photographing.　Sixty-eight　points　from　2-dimensional　facial　photos　were　labelled　by　automated　identification.　An　optimized　model　with　facial　features　and　basic　clinical　information　was　established　and　tenfold　cross-validation　was　performed.　Area　under　the　receiver　operating　characteristic　curve　(AUC)　indicated　the　model＇s　performance　using　sleep　monitoring　as　the　reference　standard.ResultsA　total　of　653　subjects　(77.2%　males,　55.3%　OSA)　were　analyzed.　CATBOOST　was　the　most　suitable　algorithm　for　OSA　classification　with　a　sensitivity,　specificity,　accuracy,　and　AUC　of　0.75,　0.66,　0.71,　and　0.76　respectively　(P　＜　0.05),　which　was　better　than　STOP-Bang　questionnaire,　NoSAS　scores,　and　Epworth　scale.　Witnessed　apnea　by　sleep　partner　was　the　most　powerful　variable,　followed　by　body　mass　index,　neck　circumference,　facial　parameters,　and　hypertension.　The　model＇s　performance　became　more　robust　with　a　sensitivity　of　0.94,　for　patients　with　frequent　supine　sleep　apnea.ConclusionThe　findings　suggest　that　craniofacial　features　extracted　from　2-dimensional　frontal　photos,　especially　in　the　mandibular　segment,　have　the　potential　to　become　predictors　of　OSA　in　the　Chinese　population.　Machine　learning-derived　automatic　recognition　may　facilitate　the　self-help　screening　for　OSA　in　a　quick,　radiation-free,　and　repeatable　manner.