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Evolution of Facial Measurement Technology and Its Prospects with the Development of Artificial Intelligence
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摘要:
面容测量在临床诊断和异常面容识别中具有重要意义。随着人体测量学的发展, 面容测量学已成为独立的研究领域, 并广泛应用于整形外科和颅颌面外科等学科。本文梳理了面容测量学的发展历程, 并探讨在人工智能背景下, 面容测量学的未来发展趋势。目前, 3D体表成像技术可准确捕捉和重建面部软组织的立体形态, 提高测定的精确性和客观性, 成为新的面容测量金标准, 不仅为疾病诊断和手术规划提供了参考, 且在美容效果评价和衰老研究中发挥重要作用。近年来人工智能技术发展迅猛, 可实现对异常面容的直接识别。基于二维图像的面容识别系统已相对成熟, 但受限于信息维度, 难以全面捕捉面部特征; 基于三维图像识别的准确度虽高, 却受限于训练样本数量, 在异常面容的识别与分类中仍面临挑战。人工智能与面容测量学的结合有效推动了面部标记点自动识别技术的发展, 为疾病面容评估提供了更为精确和可解释的方法。未来研究应聚焦于构建可靠的三维面容数据库, 以进一步提升面容识别的准确性; 同时, 应开发基于小样本的面容识别体系, 从而为罕见病和特殊疾病的面容识别提供有力支持。
Abstract:Facial anthropometry has profound importance in clinical diagnosis and the recognition of abnormal facial features. With the development of anthropometry, facial anthropometry has emerged as an independent research field and is widely applied in disciplines such as plastic surgery and cranio-maxillofacial surgery. This paper reviews the evolution of facial anthropometry and discusses its future trends in the context of artificial intelligence (AI). Currently, 3D facial imaging technology can accurately capture and reconstruct the three-dimensional morphology of facial soft tissues, and enhance the precision and objectivity of measurements, thus becoming the new "gold standard" for facial anthropometry. It not only provides reference for disease diagnosis and surgical planning but also plays a crucial role in evaluating cosmetic outcomes and aging research. In recent years, AI technology has developed rapidly, enabling direct recognition of abnormal facial features. Although facial recognition systems based on two-dimensional images are relatively mature, they have to struggle to fully capture facial features as they are limited by the dimensionality of information. While three-dimensional image-based recognition boasts high accuracy, it faces challenges in the recognition and classification of abnormal facial features due to limitations in the number of training samples. The integration of AI and facial anthropometry has effectively promoted automatic recognition technology for facial landmarks, thus providing more precise and interpretable methods for assessing disease-related facial features. Future research should focus on building reliable three-dimensional facial databases to further improve the accuracy of facial recognition. Additionally, developing facial recognition systems based on small sample sizes is necessary to provide robust support for the recognition of facial features associated with rare and special diseases.
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Keywords:
- human phenotype /
- facial morphometry /
- facial recognition /
- facial diagnosis
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房颤是临床上最常见的病理性心律失常，欧美人群的发病率远高于亚洲人群[1]。房颤的发病率随年龄的增长逐步增加，20岁以上成人的发病率约为3%，预计55岁时房颤的发病率高达37%[2]。高血压、心力衰竭、冠心病、糖尿病和慢性肾功能不全患者的房颤发病率更高，其中约15%~20%的慢性肾功能不全患者合并房颤[3]，而房颤患者中约50%合并肾功能不全[4]。房颤是缺血性脑卒中和体循环栓塞的直接原因之一，抗凝治疗可有效减少卒中事件的发生。目前临床上常用的口服抗凝药物主要分为维生素K拮抗剂(如华法林)和非维生素K拮抗剂类口服抗凝剂(non-vitamin K antagonist oral anticoagulants，NOACs)[1](表 1)。华法林为传统的口服抗凝剂，拥有长达60余年的用药历史，其抗凝作用显著持久，价格低廉，临床应用广泛，但华法林易受食物和药物的影响，且需定期监测国际标准化比值(international normalized ratio，INR)，患者依从性不佳。而直接作用于凝血因子或凝血酶的NOACs(如达比加群酯、利伐沙班、阿哌沙班、依度沙班)起效迅速，无需定期监测INR，且受其他因素影响较小，患者依从性更佳，临床应用日益增加。对于瓣膜性房颤患者，即中重度二尖瓣狭窄或人工机械瓣膜患者，出于安全性考虑仅推荐使用华法林[1]，而对于非瓣膜性房颤患者，即排除中重度二尖瓣狭窄及人工机械瓣膜，NOACs预防卒中和深静脉血栓的效果不亚于华法林，且出血风险更低，2019年美国房颤管理指南[6]及2020年欧洲心脏病学会指南[1]将其作为非瓣膜性房颤患者的首选用药。
表 1 临床常用口服抗凝药物特征比较项目 华法林 达比加群酯 利伐沙班 阿哌沙班 依度沙班 机制 维生素K拮抗剂 直接凝血酶拮抗剂 凝血因子Ⅹa拮抗剂 凝血因子Ⅹa拮抗剂 凝血因子Ⅹa拮抗剂 代谢 99%肝脏 80%肾脏 33%肾脏 27%肾脏 50%肾脏 血浆蛋白结合率 99% 35% 95% 87% 55% 透析清除率 ＜1% 50%~60% ＜1% 6% 9% FDA批准可应用的CrCl阈值[5](mL/min) 无 15 15 无 15 相较于华法林卒中的风险比[5](95% CI) 参照 0.56(0.37~0.85) 0.88(0.65~1.19) 0.79(0.55~1.14) 0.87(0.65~1.18) 相较于华法林大出血的风险比[5](95% CI) 参照 1.01(0.79~1.30) 0.98(0.84~1.14) 0.50(0.38~0.66) 0.76(0.58~0.98) FDA：美国食品药品监督管理局；CrCl：肌酐清除率 慢性肾功能不全患者由于机体代谢降低而导致药物在体内蓄积，出血风险增加。NOACs均不同程度地通过肾脏代谢，因此相关Ⅲ期药物临床试验未将肌酐清除率(creatinine clearance rate，CrCl)＜25 mL/min的患者纳入研究[7-10]。目前，口服抗凝剂在非瓣膜性房颤伴慢性肾功能不全患者中的应用尚存在争议，本文对非瓣膜性房颤伴慢性肾功能不全患者口服抗凝剂的应用进展进行总结，以期为临床实践提供参考。
1. 用药前评估
慢性肾功能不全患者发生卒中和出血的风险均较正常人群增高，其原因不仅在于慢性肾功能不全和卒中有共同的高危因素，如老年、糖尿病、高血压、高脂血症、吸烟等，而且肾病所引起的氧自由基超载、交感神经过度激活、高同型半胱氨酸血症、尿毒症毒素和水钠潴留等进一步增加了卒中的发生风险[11]。Framingham心脏研究发现估算的肾小球滤过率(estimated glomerular filtration rate，eGFR)＜60 mL/ (min·1.73 m2)的人群卒中发生风险明显高于eGFR≥60 mL/ (min·1.73 m2)者[12]。另一项关于慢性肾功能不全患者卒中发生风险的荟萃分析进一步证实，eGFR＜60 mL/ (min·1.73 m2)的患者卒中发生风险增加43%，且肾功能不全患者的卒中预后更差、死亡率更高[13]。此外，慢性肾功能不全患者白蛋白降低，药物多以游离状态存在于血液中，增加了抗凝剂引发出血的风险。同时，尿毒症诱导的血小板破坏、血液透析时频繁的导管操作、透析膜的滤过作用以及肝素的应用等也进一步增加了肾病患者的出血风险[11]。
1.1 卒中风险评估
目前临床上最常用的非瓣膜性房颤患者卒中风险评估量表是CHA2DS2-VASc评分[1]，其中具有充血性心力衰竭病史(congestive heart failure history)、高血压病史(hypertension history)、年龄介于65~74岁(age)、糖尿病病史(diabetes history)、女性(sex)、血管疾病(vascular disease history)分别评分为1分；年龄大于74岁，具有卒中或短暂性脑缺血发作或血栓栓塞病史评分为2分，否则为0分。对于CHA2DS2-VASc评分为0分的男性和评分为1分的女性，不建议采取抗凝治疗，但对于CHA2DS2-VASc评分≥2分的男性和评分≥3分的女性建议采取抗凝治疗[1]，CHA2DS2-VASc评分预测非瓣膜性房颤患者卒中发生风险具有较高的灵敏度和特异度(94.2%和95.5%)[14]。
1.2 出血风险评估
2020年欧洲心脏病学会指南推荐采用HAS-BLED评分量表评估口服抗凝剂患者的出血风险[1]，其中难以控制的高血压(hypertension)、肝功能异常(abnormal liver function)、肾功能异常(abnormal renal function)、卒中史(stroke)、出血史或出血倾向(bleeding)、INR不稳定(labile INRs)、老年(elderly)、药物(drugs)、酗酒(drink)分别评分为1分，评分≥3分提示出血风险高，HAS-BLED评分量表可有效预测65%以上的出血事件[15]。但研究显示，HAS-BLED评分高的患者口服抗凝剂的临床获益仍大于出血风险，提示此类患者不应禁用抗凝剂，而应纠正可调节的出血危险因素，并积极监测出血风险[1]。
2. 非瓣膜性房颤伴慢性肾功能不全患者口服抗凝剂的应用
2.1 轻中度肾功能不全
根据2019年美国房颤管理指南[6]、2020年欧洲心脏病学会指南[1]及2021年欧洲心律协会指南[16]，华法林和NOACs均可用于非瓣膜性房颤患者的抗凝治疗(I类推荐)，首选NOACs。
华法林可降低非瓣膜性房颤伴轻度肾功能不全患者的卒中发生风险[17]，但华法林的疗效和安全性与INR的达标率密切相关。一项比较固定剂量华法林联合阿司匹林与INR调控的华法林预防卒中疗效的随机对照研究发现，对于非瓣膜性房颤伴慢性肾功能不全Ⅲ期患者[eGFR为30~60 mL/ (min·1.73 m2)]，在INR调控下应用华法林优于固定剂量华法林联合阿司匹林，可有效减少76%的缺血性脑卒中和体循环栓塞事件[18]。对于存在基础肾病且INR控制不佳的患者(INR＞3.0)，应用华法林可能导致慢性肾病加速恶化和急性肾损伤，即华法林相关性肾病[19-21]。因此，应用华法林的患者应严格控制INR于2.0~3.0，并定期检测肾功能。
4种NOACs的Ⅲ期药物临床试验均纳入了CrCl＞30 mL/min的患者，其中阿哌沙班纳入CrCl＞25 mL/min的患者。NOACs预防卒中和栓塞的效果不亚于华法林，且显著降低出血风险，尤其是颅内出血风险[7-10]。一项比较阿哌沙班与华法林疗效及安全性的临床试验研究显示，阿哌沙班降低卒中、体循环栓塞的效果优于华法林，且降低了大出血的发生风险，尤其是颅内出血风险和全因死亡率[7]。在比较利伐沙班与华法林疗效及安全性的临床试验中，利伐沙班预防栓塞的疗效不亚于华法林，但使用利伐沙班的患者发生颅内出血和致命性出血的风险更低[8]。此后，研究者又进一步比较了服药期间患者的肾功能变化情况，相较于利伐沙班，应用华法林的患者CrCl下降更显著，提示对于易发生抗凝剂相关性肾病的患者，利伐沙班优于华法林[22]。关于达比加群酯疗效及安全性的临床试验研究显示，相较于华法林，150 mg达比加群酯减少了35%的卒中和体循环栓塞发生风险，且不增加大出血的发生风险；110 mg达比加群酯预防脑卒中和体循环栓塞的效果不亚于华法林，且降低了20%的大出血事件[9]。比较2种剂量的依度沙班(30 mg/60 mg)与华法林疗效及安全性的临床试验研究发现，高剂量的依度沙班疗效不亚于华法林且显著降低出血风险，但低剂量的依度沙班疗效劣于华法林，建议采用高剂量的依度沙班[10]。另一项相似的随机双盲对照临床试验研究显示，对于CrCl为30~90 mL/min的患者，依度沙班可使患者明显获益，但对于CrCl＞95 mL/min的患者，应用高剂量的依度沙班将增加卒中的发生风险，其原因可能在于依度沙班主要依赖肾脏代谢，肾功能较好的患者药物清除率过高，疗效降低[23]。
2.2 重度肾功能不全
2019年美国房颤管理指南[6]和2020年欧洲心脏病学会指南[1]基于现有的观察性研究，建议非瓣膜性房颤伴重度肾功能不全的患者(CrCl为15~30 mL/min)考虑应用华法林进行抗凝治疗。一项纳入14 892例66岁以上新发房颤患者的回顾性研究发现，非瓣膜性房颤伴重度肾功能不全的患者应用华法林可降低36%的不良事件且不增加出血风险[24]。
目前，关于NOACs在非瓣膜性房颤伴重度肾功能不全患者中的应用研究数量有限，且多为观察性研究。近期，一项比较阿哌沙班与华法林在非瓣膜性房颤伴重度肾功能不全患者中应用的随机对照研究提示，阿哌沙班相较于华法林更少发生出血事件，更为安全[25]。但该研究并未对比两种药物的疗效且仅局限于CrCl为25~30 mL/min的患者，未纳入CrCl＜25 mL/min的患者。不同指南对于此类患者应用NOACs的意见也存在差异，2019年美国房颤管理指南[6]建议减量使用达比加群酯、利伐沙班、阿哌沙班或依度沙班，而2021年欧洲心律协会指南[16]则不建议使用达比加群酯，可考虑使用利伐沙班、依度沙班或阿哌沙班。
2.3 终末期肾病或透析
根据2019年美国房颤管理指南[6]，对于卒中高风险的非瓣膜性房颤伴终末期肾病(CrCl＜15 mL/min)或透析的患者可考虑应用华法林，并严格控制INR于2.0~3.0(Ⅱ类推荐)。阿哌沙班是唯一可用于高卒中风险的非瓣膜性房颤伴终末期肾病或透析患者的NOACs药物(Ⅱ类推荐)，不建议此类患者使用利伐沙班、达比加群酯、依度沙班(Ⅲ类推荐)。
目前对于此类患者抗凝剂的应用均基于观察性或药物动力学研究，且结论存在较大分歧。丹麦的一项回顾性队列研究中发现，对于行肾脏替代治疗的非瓣膜性房颤患者，华法林可有效降低其卒中发生风险，但增加出血风险[26]。另一项针对行血液透析的非瓣膜性房颤患者应用华法林的疗效及安全性的回顾性队列研究发现，华法林可降低全因死亡率和缺血性脑卒中的发生风险，且不增加出血风险，其中INR控制于2.0~3.0的患者获益最大[27]。
但相较于支持华法林的有限研究，大部分观察性研究和荟萃分析结果并不支持应用华法林。加拿大一项纳入1600例行血液透析的非瓣膜性房颤患者的回顾性队列研究显示，华法林不仅不能降低透析患者卒中的发生风险，且增加44%的出血风险[28]。另一项基于1671例行血液透析的非瓣膜性房颤患者的回顾性研究显示，应用华法林增加90%的卒中风险，其中INR控制不佳患者的卒中发生风险最高[29]。中国台湾的一项基于电子病历的研究发现，对于终末期肾病或透析的非瓣膜性房颤患者，应用华法林者较未应用者具有更高的出血和栓塞发生风险[30]。此外，一项纳入超过48 500例非瓣膜性房颤伴肾功能不全患者的荟萃分析显示，非瓣膜性房颤伴终末期肾病的患者使用华法林不能降低卒中的发生风险，同时增加大出血风险，不建议此类患者使用华法林[31]。同时，另一项纳入31 321例行血液透析的非瓣膜性房颤患者的荟萃分析发现，应用华法林增加45%的卒中发生风险[32]。
阿哌沙班在非瓣膜性房颤伴终末期肾病或透析患者中的应用多基于小样本的药代动力学研究。一项关于5 mg阿哌沙班的药代动力学研究发现，透析后予非瓣膜性房颤患者5 mg阿哌沙班，其暴露剂量为肾功能正常患者的1.36倍，但两者的血药浓度峰值无显著差异[33]，提示透析患者可使用阿哌沙班。另一项关于10 mg阿哌沙班的药代动力学研究中，依据建立的回归模型计算出CrCl为15 mL/min的患者药物暴露剂量较肾功能正常患者高44%[34]，但肾功能不全不影响阿哌沙班的最高血药浓度，终末期肾病患者可使用10 mg阿哌沙班而无需调整剂量。
现有的观察性研究不支持终末期肾病或透析患者使用利伐沙班、达比加群酯或依度沙班。一项基于美国终末期肾病患者的观察性研究发现，对于行血液透析的非瓣膜性房颤患者，应用利伐沙班、达比加群酯可显著增加出血事件，其中利伐沙班较华法林增加了38%的大出血风险及58%的致死性出血风险，且利伐沙班不易被透析膜滤出这一特点使得患者长期处于过度抗凝状态[5]，尽管达比加群酯可在透析中被滤出，但相较于华法林增加了48%的大出血风险及88%的致死性出血风险，且周期性透析导致达比加群酯的血药浓度极不稳定，患者反复波动于抗凝不足与抗凝过度状态[5]。
3. 口服抗凝剂相关出血的处理
对于口服抗凝剂发生活动性出血的患者，可按压出血部位，并评估患者的血流动力学、血压、凝血功能、肾功能等指标，根据患者出血严重程度采取相应治疗措施。对于少量出血患者，可暂缓抗凝剂的应用；对于中重度出血患者，可采取对症治疗；对于威胁生命的大出血，则考虑使用抗凝剂的拮抗剂或紧急输入凝血酶原复合物[1]。
4. 小结
非瓣膜性房颤伴慢性肾功能不全患者的抗凝治疗是临床工作面临的一项挑战。临床医生应依据患者不同的肾功能等级合理选择抗凝药物，对于轻中度肾功能不全的非瓣膜性房颤患者，华法林和NOACs均可应用，首选NOACs；对于重度肾功能不全的非瓣膜性房颤患者，可考虑减量使用华法林或选择性减量使用NOACs；对于终末期肾病或透析的非瓣膜性房颤患者，应根据病情慎重考虑使用华法林或小剂量的阿哌沙班，禁用达比加群酯、利伐沙班和依度沙班。
作者贡献：刘润竹负责查阅文献、撰写论文；龙笑负责论文修订。利益冲突：所有作者均声明不存在利益冲突 -
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图 1 面容测量学发展时间线总结
注：黄色为直接测量方法相关事件，蓝色为2D测量法相关事件，红色为3D测量法相关事件，紫色为人工智能与面容测量学结合相关事件
Figure 1. Timeline summary of the development of facial metrology
Note: Yellow for events related to direct measurement; Blue for events related to 2D measurement; Red for events related to 3D measurement; Purple for events related to the combination of AI and facial measurements
表 1 三种3D体表成像设备的优劣势比较
Table 1 Comparison of advantages and disadvantages of three 3D imaging devices
设备类型 原理 优势 缺点 代表设备 误差 激光扫描 激光束投射至面容表面，产生干涉条纹 成像速度快、应用范围广、对人体无害 准确性较低，成本高、体积大 Minolta Vivid 较大 结构光扫描 结构光栅条纹投射至面容表面，基于光学三角测量 操作方便、可捕捉运动对象、费用低、便携 面容还原度较低，测量精度低 FaceSCAN 1 mm以内 立体摄影 双目视觉原理，从多个角度捕捉面部特征并进行拼接 还原软组织表面颜色、纹理，成像速度快、环境要求低、数据存储方便 受被测量者面部运动的影响，需进行校准，价格昂贵 3dMDface、Vectra H 1 mm以内 
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表 2 人工智能在面容评估中的应用总结
Table 2 Summary of artificial intelligence applications in facial assessment
年份 第一作者 技术名称 识别对象 实现方法 基于图像类型 识别准确性 2018 Song[70] GES(AGing pattErn Subspace) 特纳综合征儿童面容 PCA+SVM 2D 84.6% 2019 Gurovich[66] DeepGestalt 遗传综合征及Noonan综合征亚型 计算机视觉和深度学习 2D 91.0% 2020 Bannister[79] NICP(Non-rigid Iterative Closest Point) 健康成年受试者面容标记点 基于成熟2D图像识别算法的3D投射 3D 平均误差2.5 mm 2021 Pan[74] - 特纳综合征儿童面容 DCNN 2D 97.3% 2022 Fu[81] - 胎儿酒精综合征 基于面部标记点的深度学习 3D 误差为0.59~1.58像素 2022 Hsieh[84] GestaltMatcher 罕见疾病 有监督DCCN 2D 78.1% 2022 Bannister[85] - 多种遗传综合征 贝叶斯分类器 3D 71% 2022 Rouxel[86] DeepGestalt 歌舞伎综合征及亚型 DCCN 2D 85.0% 2022 Hartmann[77] - 面部表情 ANN 3D 81.2% 2023 Bannister[19] - 多种遗传综合征 2D: CNN
3D: PCA2D+3D 2D: 68.0%
3D: 81.2%2023 Jiang[83] PointNet++ 半侧颜面萎缩患者面部标记点 CNN 3D 96.0% 2024 Chong[82] UNET 健康、肢端肥大、局限性硬皮病面容标记点 CNN 3D 平均误差1.4 mm 2024 Yang[87] 高分辨网络关键点检测 唇周标记点 深度学习 3D 平均误差1.3 mm PCA(principal component analysis)：主成分分析；SVM(support vector machine)：支持向量机；DCNN(deep convolutional neural networks)：深度卷积神经网络；ANN(artificial neural networks)，人工神经网络；CNN(convolutional neural networks)：卷积神经网络；-: 未提及
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表 3 面容测量学方法比较
Table 3 Comparison of different facial measuring methods
年份 第一作者 研究对象 2D测量 3D测量 直接测量 人工智能 结果 1995 Aung[34] 30名受试者面部43个标记点 - *(激光扫描) * - 唇周、鼻周误差＜1 mm 1996 Gross[89] 不同表情下面部运动幅度，4名受试者面部15个标记点 * * - - 二维分析可能不足以评估最大动画期间的面部运动，三维分析可能更适合检测面部功能的临床差异 2006 Kovacs[39] 5名受试者面部48个标记点 - *(激光扫描) * - 误差超过2 mm 2006 Goos[90] 2名受试者面部7个标记点之间的距离 * *(激光扫描) - - 两种方法的差异较大 2007 Ghoddousi[17] 6名受试者面部14个标记点 * *(立体摄影) * - 3D测量法、直接测量法的重复性更好，2D测量结果变异性大，3D测量结果偏大 2011 Aynechi[31] 10名受试者面部18个线性指标 - *(立体摄影) * - 3D测量系统的精度较高，误差＜2 mm 2013 Al-Anezi[40] 32名受试者面部23个标记点 - *(结构光扫描) - *(software) 平均误差＜0.55 mm 2014 Kook[16] 12个模型的面部15个标记点 - *(立体摄影) * - 测量误差＜0.7 mm，测量准确性、重复性均可 2014 Berssenbrügge[91] 50名受试者面部对称性指数 * *(立体摄影) - - 2D测量、3D测量下的对称性指数互相存在关联 2016 Ye[42] 10名受试者面部标记点 - *(立体摄影与结构光扫描) * - 两种3D测量法均可准确还原真实测量结果，其中立体摄影的可靠性更高 2016 Dindarogğlu[18] 80名受试者10个面部参数 * *(立体摄影) * - 3D测量法是最准确可靠的方法 2018 Gibelli[37] 50名受试者及人面部模型的面部标记点 - *(激光扫描与立体摄影) - - 激光扫描仪在模型扫描中可靠，在受试者中不可靠 2023 Gašparović[92] 39名受试者面部线性指标，术后自动取点 - *(立体摄影) - *(ICP) 自动取点测量的可重复性较高，与真实差距小 2023 Jiang[83] 135名受试者面部32个标记点 - *(立体摄影) - *(PointNet++，卷积神经网络) 平均误差1.46 mm，较准确 2023 Pellitteri[46] 30名受试者面部线性指标及分区 - *(立体摄影、结构光、智能手机应用程序) - - 三种方法的结果无明显差异 2024 Chong[82] 100名受试者面部20个标记点 - *(立体摄影) - *(CNN-UNET) 健康人群中平均误差为1.4 mm，疾病人群中误差增大 2024 Yang[87] 120名受试者唇周标记点 - *(立体摄影) - *(深度学习) 线性测量的平均误差为0.87 mm，角度测量的平均误差为5.62° *: 参与比较项目；-：该项未涉及
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