人工智能在中医药领域的应用进展及现状思考

人工智能（Artificial Intelligence，AI）的蓬勃兴起为现代社会带来了前所未有的机遇，中医药是中华民族传承千年的文化瑰宝。随着人工智能技术不断在中医药领域的科技创新中崭露头角，二者的融合不断加深，人工智能在中医药领域的发展前景、争议挑战也引发了诸多思考。本文将从人工智能在中医药领域的应用入手，对人工智能辅助中医诊断、智能决策与数据挖掘、健康管理及中草药现代化研究等方面，就近年来国内外研究进展进行总结与分析，以期为AI视域下实现中医药现代化、智能化赋能。     

DHS和PFNA固定治疗老年股骨粗隆间骨折的疗效

目的对比动力髋螺钉(dynamic hip screw,DHS)治疗和股骨近端抗旋髓内钉(proximal femoral nail anti-rotation,PFNA)固定治疗老年股骨粗隆间骨折疗效。方法纳入本院收治的45例采用PFNA固定治疗的老年股骨粗隆间骨折患者作为研究组,研究时间为2017年11月-2018年11月;回顾性分析2016年11月-2017年11月在我院采用DHS治疗的45例老年股骨粗隆间骨折患者的临床资料,作为对照组。记录两组的相关手术指标,观察手术并发症情况。结果研究组的手术时间和骨折愈合时间均短于对照组,术中出血量明显少于对照组,术后疼痛VAS评分均明显低于对照组(P<0.05)。对照组的并发症发生率高于研究组(P<0.05)。结论PFNA固定治疗比DHS治疗老年股骨粗隆间骨折更加安全有效,值得临床借鉴和应用。     

改良三点式重睑成形术的临床应用研究

目的:探讨改良三点式重睑术的临床应用疗效。方法:2012年6月-2019年6月,共165例单睑患者采用了改良三点式重睑术,沿术前标记线将三点处皮肤切开,剪刀将切口下唇的眼轮匝肌适当去除,同时将切口与切口在皮下层打通,剪除切口与切口之间的眼轮匝肌,6-0可吸收线挂睑板前筋膜或提上睑肌腱膜及切口下唇皮下组织缝合,三点切口各缝1针。再用6-0单丝尼龙线按照常规重睑线缝合方法挂切口下唇皮肤、睑板前筋膜或提上睑肌腱膜及切口上唇皮肤缝合打结,三点切口各缝1针。伴内眦赘皮者同时行内眦赘皮矫正术。结果:152例患者获得随访,随访患者大部分获得了比较满意的重睑,睁眼重睑流畅、自然,闭眼刀口痕迹不明显。2例患者出现内侧重睑线变浅,1例患者出现外侧重睑线变浅,所有患者均未出现重睑消失。5例患者双侧重睑线有轻度不对称。患者总体满意率为94.7%(144/152)。结论:改良三点式重睑术具有创伤小、并发症少、效果逼真、不易脱落、手术痕迹不明显等优点,值得推广应用。     

后外侧结构重建在新鲜股骨颈骨折人工股骨头置换术中的应用

目的探讨后外侧结构重建对后外侧入路人工股骨头置换术术后早期关节脱位的影响。方法选取2016年9月至2017年8月于我院行后外侧入路初次人工股骨头置换术的股骨颈骨折患者60例,根据术中是否修补关节囊及外旋肌群分为重建组(33例:舌形切开关节囊,术中将关节囊及外旋肌群原位缝合在大转子后方及臀中肌肌腱附着处)和对照组(27例:切除关节囊后,术中未进行外旋肌群修复重建)。比较两组的手术情况及术后近期关节功能情况。结果重建组的手术时间为(45.0±15.3) min,长于对照组的(35.0±12.4) min (P <0.05)。重建组术腔引流量为(200.0±80.0) m L,少于对照组的(420.0±120.6) m L (P <0.05)。重建组的早期脱位率为0.000%(0例),与对照组的7.407%(2例)比较无统计学差异(P>0.05)。重建组术后Harris评分为(92.0±3.4)分,高于对照组的(88.2±5.0)分(P <0.05)。结论在后外侧入路人工股骨头置换过程中行后外侧结构重建能够有效减少术腔引流量,提高髋关节Harris评分,对维持髋关节软组织平衡具有重要意义。     

Assisting students with autism to cooperate with their peers to perform computer mouse collaborative pointing operation on a single display simultaneously

The purpose of this study was to provide students with autism spectrum disorder (ASD) the chance to cooperate with their peers to perform computer mouse collaborative pointing operation. In this study, we adopted the Single Display Groupware (SDG) concept to develop the Multiple Cursor Collaborative Operating Program (MCCOP) software, which allows multiple users to operate a single computer simultaneously without interfering with each other. With the implementation of MCCOP software, users control their own cursors to perform a function in their respective cursor moving areas on a single display. A collaborative pointing test software (CPTS) program was designed in this experiment to evaluate participants’ collaborative pointing performance. This study adopted an ABAB design, and the experimental results show that all participants significantly increased their collaborative pointing performance during the intervention phase, compared to the baseline phase. Practical and developmental implications of the findings are discussed.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.