Advances in the application of deep learning to the risk assessment of nerve damage associated with extraction of impacted mandibular third molars
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国家科技期刊平台
NETL
NSTL
万方数据
随着数字医学的发展,深度学习(deep learning,DL)在口腔医学领域中的应用日益广泛,诸多研究者已逐步将其应用在下颌阻生智齿(impacted mandibular third molar,IMTM)的拔除术中,并将术前影像学检查如X线片、锥形束CT(cone beam CT,CBCT)等与DL结合来辅助医生进行诊断和决策.下牙槽神经(inferior alveolar nerve,IAN)损伤是IMTM拔除术后最严重的并发症之一,DL有望协同影像学检查为医生提供更为客观、准确的神经损伤风险评估意见,提高治疗效果.本文将对现阶段DL在IMTM拔除术的术前影像识别、术前辅助诊断与评估及神经损伤预后预测中的应用进行综述,并对未来DL在IMTM拔除术中的作用进行展望.
The application of deep learning(DL)has become widespread with the development of digital medicine.At present,DL has been gradually applied to the fields of stomatology.Multiple studies have applied DL,combined with preoperative examination images such as X ray and cone beam CT(CBCT)images,to assist clinical diagnosis and decision-making in dealing with impacted mandibular third molar(IMTM).Besides,inferior alveolar nerve(IAN)injury is one of the most serious sequelae after extraction of IMTM.Combined with imageological examination,DL can provide objective and accurate estimation of the risk of IAN injury to improve the outcome of treatment.This paper reviews the current application of DL in preoperative image recognition,preoperative auxiliary diagnosis and evaluation,and IAN injury prognosis prediction in the extraction of IMTM,and looked into the role of DL in the extraction of IMTM in the future.
deep learningimpacted mandibular third molartooth extractionorthopantomogramcone beam CTinferior alveolar nerve injury
国家科技期刊平台
NSTL
万方数据
