Asci Francesco, Scardapane Simone, Zampogna Alessandro, D'Onofrio Valentina, Testa Lucia, Patera Martina, Falletti Marco, Marsili Luca, Suppa Antonio
IRCCS Neuromed Institute, Pozzilli, Italy.
Department of Information, Electronic and Communication Engineering (DIET), Sapienza University of Rome, Rome, Italy.
Front Aging Neurosci. 2022 May 6;14:889930. doi: 10.3389/fnagi.2022.889930. eCollection 2022.
Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders.
One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm.
Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83).
Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms.
书写是一种通过广泛的脑网络激活而获得的复杂认知和运动技能。因此，书写可能提供与健康状况相关的生物学信息。此外，通过安全、廉价且广泛可用的工具，可以在自然场景中轻松收集书写样本。因此，通过人工智能进行客观的笔迹分析将代表一种用于健康受试者和患有神经系统疾病的人群的远程医疗目的的创新策略。
招募了156名健康受试者（61名男性；年龄49.6±20.4岁）并根据年龄分为三个亚组：年轻成年人（YA）、中年成年人（MA）和老年人（OA）。参与者执行了一项通过智能手机数字化的自然书写任务。数据经过DBNet算法以测量和比较三组中的平均笔画大小。还使用卷积神经网络（CNN）对手写样本进行分类。最后，计算受试者工作特征（ROC）曲线以及敏感性、特异性、阳性、阴性预测值（PPV、NPV）、准确性和曲线下面积（AUC）以报告算法的性能。
老年人的笔画大小明显小于中年成年人和年轻成年人。CNN分类器客观地区分了年轻成年人与老年人（敏感性 = 82%，特异性 = 80%，PPV = 78%，NPV = 79%，准确性 = 77%，AUC = 0.84）、中年成年人与老年人（敏感性 = 84%，特异性 = 56%，PPV = 78%，NPV = 73%，准确性 = 74%，AUC = 0.7）以及年轻成年人与中年成年人（敏感性 = 75%，特异性 = 82%，PPV = 79%，NPV = 83%，准确性 = 79%，AUC = 0.83）。
书写能力会随着人类衰老而逐渐下降。使用机器学习算法可以远程且客观地检测生理衰老对书写能力的影响。
