Kidney cancer is a global health concern, and accurate assessment of patient
frailty is crucial for optimizing surgical outcomes. This paper introduces AI
Age Discrepancy, a novel metric derived from machine learning analysis of
preoperative abdominal CT scans, as a potential indicator of frailty and
postoperative risk in kidney cancer patients. This retrospective study of 599
patients from the 2023 Kidney Tumor Segmentation (KiTS) challenge dataset found
that a higher AI Age Discrepancy is significantly associated with longer
hospital stays and lower overall survival rates, independent of established
factors. This suggests that AI Age Discrepancy may provide valuable insights
into patient frailty and could thus inform clinical decision-making in kidney
cancer treatment.

介绍了一种新的指标 AI Age Discrepancy，通过对术前腹部 CT 扫描的机器学习分析得出，作为评估肾癌患者脆弱性和术后风险的潜在指标。这项回顾性研究发现，较高的 AI Age Discrepancy 与较长的住院时间和较低的总体生存率显著相关，独立于已确立的因素。这表明 AI Age Discrepancy 可能为评估患者脆弱性提供有价值的信息，并在肾癌治疗的临床决策中发挥作用。

AI 年龄差异：肾脏肿瘤患者脆弱性评估的新参数

AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney  Tumor Patients

Otago Exercise Program (OEP) is a rehabilitation program for older adults to
improve frailty, sarcopenia, and balance. Accurate monitoring of patient
involvement in OEP is challenging, as self-reports (diaries) are often
unreliable. With the development of wearable sensors, Human Activity
Recognition (HAR) systems using wearable sensors have revolutionized
healthcare. However, their usage for OEP still shows limited performance. The
objective of this study is to build an unobtrusive and accurate system to
monitor OEP for older adults. Data was collected from older adults wearing a
single waist-mounted Inertial Measurement Unit (IMU). Two datasets were
collected, one in a laboratory setting, and one at the homes of the patients. A
hierarchical system is proposed with two stages: 1) using a deep learning model
to recognize whether the patients are performing OEP or activities of daily
life (ADLs) using a 10-minute sliding window; 2) based on stage 1, using a
6-second sliding window to recognize the OEP sub-classes performed. The results
showed that in stage 1, OEP could be recognized with window-wise f1-scores over
0.95 and Intersection-over-Union (IoU) f1-scores over 0.85 for both datasets.
In stage 2, for the home scenario, four activities could be recognized with
f1-scores over 0.8: ankle plantarflexors, abdominal muscles, knee bends, and
sit-to-stand. The results showed the potential of monitoring the compliance of
OEP using a single IMU in daily life. Also, some OEP sub-classes are possible
to be recognized for further analysis.

通过使用腰部佩戴的惯性测量单元 (IMU) 进行数据收集，并采用深度学习模型，本研究提出了一个无障碍且准确的系统来监测老年人的 Otago 运动计划。研究结果表明，在实验室和家庭环境中，使用单个 IMU 可以对 Otago 运动计划及其子类进行准确的监测。

使用单一 IMU 和分层机器学习模型对老年人进行奥塔哥运动监测

Otago Exercises Monitoring for Older Adults by a Single IMU and  Hierarchical Machine Learning Models

The growing global elderly population is expected to increase the prevalence
of frailty, posing significant challenges to healthcare systems. Frailty, a
syndrome associated with ageing, is characterised by progressive health
decline, increased vulnerability to stressors and increased risk of mortality.
It represents a significant burden on public health and reduces the quality of
life of those affected. The lack of a universally accepted method to assess
frailty and a standardised definition highlights a critical research gap. Given
this lack and the importance of early prevention, this study presents an
innovative approach using an instrumented ink pen to ecologically assess
handwriting for age group classification. Content-free handwriting data from 80
healthy participants in different age groups (20-40, 41-60, 61-70 and 70+) were
analysed. Fourteen gesture- and tremor-related indicators were computed from
the raw data and used in five classification tasks. These tasks included
discriminating between adjacent and non-adjacent age groups using Catboost and
Logistic Regression classifiers. Results indicate exceptional classifier
performance, with accuracy ranging from 82.5% to 97.5%, precision from 81.8% to
100%, recall from 75% to 100% and ROC-AUC from 92.2% to 100%. Model
interpretability, facilitated by SHAP analysis, revealed age-dependent
sensitivity of temporal and tremor-related handwriting features. Importantly,
this classification method offers potential for early detection of abnormal
signs of ageing in uncontrolled settings such as remote home monitoring,
thereby addressing the critical issue of frailty detection and contributing to
improved care for older adults.

使用带有传感器的墨水笔对书写进行生态评估，以年龄组分类的创新方法展示了卓越的分类器性能，可以在非受控环境（如远程居家监测）中早期检测衰老的异常迹象，从而解决衰弱检测的关键问题，为老年人改善护理做出贡献。