A detection model of cognitive impairment via the integrated gait and eye movement analysis from a large Chinese community cohort.

INTRODUCTION: Whether the integration of eye-tracking, gait, and corresponding dual-task analysis can distinguish cognitive impairment (CI) patients from controls remains unclear. METHODS: One thousand four hundred eighty-one participants, including 724 CI and 757 controls, were enrolled in this study. Eye movement and gait, combined with dual-task patterns, were measured. The LightGBM machine learning models were constructed. RESULTS: A total of 105 gait and eye-tracking features were extracted. Forty-six parameters, including 32 gait and 14 eye-tracking features, showed significant differences between two groups (P < 0.05). Of these, the Gait_3Back-TurnTime and Dual-task cost-TurnTime patterns were significantly correlated with plasma phosphorylated tau 181 (p-tau181) level. A model based on dual-task gait, dual-task smooth pursuit, prosaccade, and anti-saccade achieved the best area under the receiver operating characteristics curve (AUC) of 0.987 for CI detection, while combined with p-tau181, the model discriminated mild cognitive impairment from controls with an AUC of 0.824. DISCUSSION: Combining dual-task gait and dual-task eye-tracking analysis is feasible for the detection of CI. HIGHLIGHTS: This is the first study to report the efficiency of integrated parameters of dual-task gait and eye-tracking for cognitive impairment (CI) detection in a large cohort. We identified 46 gait and eye-tracking features associated with CI, and two were correlated to plasma phosphorylated tau 181. We constructed the model based on dual-task gait, smooth pursuit, prosaccade, and anti-saccade, achieving the best area under the curve of 0.987 for CI detection.

Deep Learning-Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation.

BACKGROUND: Neurodegenerative diseases (NDDs) are prevalent among older adults worldwide. Early diagnosis of NDD is challenging yet crucial. Gait status has been identified as an indicator of early-stage NDD changes and can play a significant role in diagnosis, treatment, and rehabilitation. Historically, gait assessment has relied on intricate but imprecise scales by trained professionals or required patients to wear additional equipment, causing discomfort. Advancements in artificial intelligence may completely transform this and offer a novel approach to gait evaluation. OBJECTIVE: This study aimed to use cutting-edge machine learning techniques to offer patients a noninvasive, entirely contactless gait assessment and provide health care professionals with precise gait assessment results covering all common gait-related parameters to assist in diagnosis and rehabilitation planning. METHODS: Data collection involved motion data from 41 different participants aged 25 to 85 (mean 57.51, SD 12.93) years captured in motion sequences using the Azure Kinect (Microsoft Corp; a 3D camera with a 30-Hz sampling frequency). Support vector machine (SVM) and bidirectional long short-term memory (Bi-LSTM) classifiers trained using spatiotemporal features extracted from raw data were used to identify gait types in each walking frame. Gait semantics could then be obtained from the frame labels, and all the gait parameters could be calculated accordingly. For optimal generalization performance of the model, the classifiers were trained using a 10-fold cross-validation strategy. The proposed algorithm was also compared with the previous best heuristic method. Qualitative and quantitative feedback from medical staff and patients in actual medical scenarios was extensively collected for usability analysis. RESULTS: The evaluations comprised 3 aspects. Regarding the classification results from the 2 classifiers, Bi-LSTM achieved an average precision, recall, and F1-score of 90.54%, 90.41%, and 90.38%, respectively, whereas these metrics were 86.99%, 86.62%, and 86.67%, respectively, for SVM. Moreover, the Bi-LSTM-based method attained 93.2% accuracy in gait segmentation evaluation (tolerance set to 2), whereas that of the SVM-based method achieved only 77.5% accuracy. For the final gait parameter calculation result, the average error rate of the heuristic method, SVM, and Bi-LSTM was 20.91% (SD 24.69%), 5.85% (SD 5.45%), and 3.17% (SD 2.75%), respectively. CONCLUSIONS: This study demonstrated that the Bi-LSTM-based approach can effectively support accurate gait parameter assessment, assisting medical professionals in making early diagnoses and reasonable rehabilitation plans for patients with NDD.

Utilization of Self-Diagnosis Health Chatbots in Real-World Settings: Case Study.

BACKGROUND: Artificial intelligence (AI)-driven chatbots are increasingly being used in health care, but most chatbots are designed for a specific population and evaluated in controlled settings. There is little research documenting how health consumers (eg, patients and caregivers) use chatbots for self-diagnosis purposes in real-world scenarios. OBJECTIVE: The aim of this research was to understand how health chatbots are used in a real-world context, what issues and barriers exist in their usage, and how the user experience of this novel technology can be improved. METHODS: We employed a data-driven approach to analyze the system log of a widely deployed self-diagnosis chatbot in China. Our data set consisted of 47,684 consultation sessions initiated by 16,519 users over 6 months. The log data included a variety of information, including users' nonidentifiable demographic information, consultation details, diagnostic reports, and user feedback. We conducted both statistical analysis and content analysis on this heterogeneous data set. RESULTS: The chatbot users spanned all age groups, including middle-aged and older adults. Users consulted the chatbot on a wide range of medical conditions, including those that often entail considerable privacy and social stigma issues. Furthermore, we distilled 2 prominent issues in the use of the chatbot: (1) a considerable number of users dropped out in the middle of their consultation sessions, and (2) some users pretended to have health concerns and used the chatbot for nontherapeutic purposes. Finally, we identified a set of user concerns regarding the use of the chatbot, including insufficient actionable information and perceived inaccurate diagnostic suggestions. CONCLUSIONS: Although health chatbots are considered to be convenient tools for enhancing patient-centered care, there are issues and barriers impeding the optimal use of this novel technology. Designers and developers should employ user-centered approaches to address the issues and user concerns to achieve the best uptake and utilization. We conclude the paper by discussing several design implications, including making the chatbots more informative, easy-to-use, and trustworthy, as well as improving the onboarding experience to enhance user engagement.

Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems.

Ongoing research efforts have been examining how to utilize artificial intelligence technology to help healthcare consumers make sense of their clinical data, such as diagnostic radiology reports. How to promote the acceptance of such novel technology is a heated research topic. Recent studies highlight the importance of providing local explanations about AI prediction and model performance to help users determine whether to trust AI's predictions. Despite some efforts, limited empirical research has been conducted to quantitatively measure how AI explanations impact healthcare consumers' perceptions of using patient-facing, AI-powered healthcare systems. The aim of this study is to evaluate the effects of different AI explanations on people's perceptions of AI-powered healthcare system. In this work, we designed and deployed a large-scale experiment (N = 3,423) on Amazon Mechanical Turk (MTurk) to evaluate the effects of AI explanations on people's perceptions in the context of comprehending radiology reports. We created four groups based on two factors-the extent of explanations for the prediction (High vs. Low Transparency) and the model performance (Good vs. Weak AI Model)-and randomly assigned participants to one of the four conditions. Participants were instructed to classify a radiology report as describing a normal or abnormal finding, followed by completing a post-study survey to indicate their perceptions of the AI tool. We found that revealing model performance information can promote people's trust and perceived usefulness of system outputs, while providing local explanations for the rationale of a prediction can promote understandability but not necessarily trust. We also found that when model performance is low, the more information the AI system discloses, the less people would trust the system. Lastly, whether human agrees with AI predictions or not and whether the AI prediction is correct or not could also influence the effect of AI explanations. We conclude this paper by discussing implications for designing AI systems for healthcare consumers to interpret diagnostic report.

Study of cognitive function in patients with severe asymptomatic carotid artery stenosis by a computerized neuropsychological assessment device.

Background: Carotid stenosis can lead to stroke and cognitive impairment. Moreover, the cognitive function was assessed mostly by paper and pencil cognitive tests. This study aimed to evaluate the impact of severe asymptomatic carotid artery stenosis (SACAS) on cognitive function by a computerized neuropsychological assessment device (CNAD). The diagnostic value of screening SACAS of the CNAD was analyzed. Methods: There were 48 patients with ≥70% asymptomatic carotid stenosis and 52 controls without carotid stenosis. Duplex ultrasound defined the degree of stenosis. The differences of cognitive function were analyzed between patients and controls. The relationship of scores of cognitive tests and age were analyzed in the linear regression equation. The diagnostic value of CNAD was evaluated by the receiver operating characteristic (ROC) curve. Results: Stenosis and control subjects had no statistically significant differences in baseline characteristics. Stenosis patients had worse scores for Stroop color-word test (p = 0.002), one back test (p = 0.013), and identification test (p = 0.006) corresponding to attention and executive ability. The analysis of linear regression equation indicated that cognitive scores of stenosis patients declined faster with age, especially for digit span test, Stroop color-word test, one back test and identification test. In analysis of ROC curve, the Stroop color-word test (p = 0.002), one back test (p = 0.013), and identification test (p = 0.006), and comprehensive index of the three tests (p = 0.001) had the diagnostic value. Conclusion: The CNAD has evaluation value and screening value for patients with cognitive impairment and SACAS. But it is necessary to update the CNAD and conduct a study with a bigger sample.

Basal Ganglia and Brainstem Located Cerebral Microbleeds Contributed to Gait Impairment in Patients with Cerebral Small Vessel Disease.

BACKGROUND: The mechanism of gait disorder in patients with cerebral small vessel disease (CSVD) remains unclear. Limited studies have compared the effect of cerebral microbleeds (CMBs) and lacunes on gait disturbance in CSVD patients in different anatomical locations. OBJECTIVE: To investigate the relationship of quantitative gait parameters with varied anatomically located MRI imaging markers in patients with CSVD. METHODS: Quantitative gait tests were performed on 127 symptomatic CSVD patients all with diffuse distributed white matter hyperintensities (WMHs). CMBs and lacunes in regard to anatomical locations and burdens were measured. The correlation between CSVD imaging markers and gait parameters was evaluated using general linear model analysis. RESULTS: Presence of CMBs was significantly associated with stride length (ß= -0.098, p = 0.0272) and right step length (ß= -0.054, p = 0.0206). Presence of CMBs in basal ganglia (BG) was significantly associated with stride length and step length. Presence of CMBs in brainstem was significantly associated with gait parameters including stride length, step length, step height, and step width. Presence of lacunes in brainstem was significantly associated with gait speed (ß= -0.197, p = 0.0365). However, presence of lacunes in the other areas was not associated with worse gait performances. CONCLUSION: BG and brain stem located CMBs contributed to gait impairment in symptomatic CSVD patients.

Patients' perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data.

Results of radiology imaging studies are not typically comprehensible to patients. With the advances in artificial intelligence (AI) technology in recent years, it is expected that AI technology can aid patients' understanding of radiology imaging data. The aim of this study is to understand patients' perceptions and acceptance of using AI technology to interpret their radiology reports. We conducted semi-structured interviews with 13 participants to elicit reflections pertaining to the use of AI technology in radiology report interpretation. A thematic analysis approach was employed to analyze the interview data. Participants have a generally positive attitude toward using AI-based systems to comprehend their radiology reports. AI is perceived to be particularly useful in seeking actionable information, confirming the doctor's opinions, and preparing for the consultation. However, we also found various concerns related to the use of AI in this context, such as cyber-security, accuracy, and lack of empathy. Our results highlight the necessity of providing AI explanations to promote people's trust and acceptance of AI. Designers of patient-centered AI systems should employ user-centered design approaches to address patients' concerns. Such systems should also be designed to promote trust and deliver concerning health results in an empathetic manner to optimize the user experience.