Kim JK, et al. J Korean Med Sci. 2022 Jan;37(6):e42. https://doi.org/10.3346/jkms.2022.37.e42

Applications of machine learning in the healthcare field have become increasingly diverse. In this review, we investigated the integration of artificial intelligence (AI) in predicting the prognosis of patients with central nervous system disorders such as stroke, traumatic brain injury, and spinal cord injury. AI algorithms have shown promise in prognostic assessment, but challenges remain in achieving a higher prediction accuracy for practical clinical use. We suggest that accumulating more diverse data, including medical imaging and collaborative efforts among hospitals, can enhance the predictive capabilities of AI. As healthcare professionals become more familiar with AI, its role in central nervous system rehabilitation is expected to advance significantly, revolutionizing patient care.

Background: Machine learning (ML) is an artificial intelligence technique in which a system learns patterns and rules from a given data. Objectives: The objective of the study was to investigate the potential of ML for predicting motor recovery in stroke patients. Methods: We analyzed data from 833 consecutive stroke patients using 3 ML algorithms: deep neural network (DNN), random forest, and logistic regression. We created a practical ML model using the most common data measured in almost all rehabilitation hospitals as input data. Demographic and clinical data, including modified Brunnstrom classification (MBC) and functional ambulation classification (FAC), were collected when patients were transferred to the rehabilitation unit (8–30 days) and 6 months after stroke onset and were used as input data. Motor outcomes at 6 months after stroke onset of the affected upper and lower extremities were classified according to MBC and FAC, respectively. Patients with an MBC of <5 and an FAC of <4 at 6 months after stroke onset were considered to have a “poor” outcome, whereas those with MBC ≥5 and FAC ≥4 were considered to have a “good” outcome. Results: The area under the curve (AUC) for the DNN model for predicting motor function was 0.836 for the upper and lower limb motor functions. For the random forest and logistic regression models, the AUCs were 0.736 and 0.790 for the upper and lower limb motor functions, respectively. The AUCs for the random forest and logistic regression models were 0.741 and 0.795 for the upper and lower limb motor functions, respectively. Conclusion: Although we used simple and common data that can be obtained in clinical practice as variables, our DNN algorithm was useful for predicting motor recovery of the upper and lower extremities in stroke patients during the recovery phase.

IoT device market has grown rapidly in recent years, with many researches have been done to investigate the IoT devices’ adoption factors. However, it is crucial to understand how to maintain the users’ continuance intention. This study, based on the post-acceptance model of the Expectation Confirmation Model (ECM), investigates the impact of early-stage users’ personal traits (personal innovativeness), social influence (normative and informational), and familiarity, on the social diffusion of IoT devices (in particular, smart speakers) measured by the continuation intention of smart speakers. We tested the research model and hypotheses using data from 364 smart speaker users, analyzed with partial least square technique using Smart-PLS. The data analysis results revealed that satisfaction has a very strong and positive impact on continuance intention. Perceived usefulness, confirmation, and personal innovativeness are positively associated with continuance intention via satisfaction. Social influence, represented by normative and informational social influence, showed a significant positive impact on continuance intention, a high degree of familiarity weakens the influence of normative social influence on continuance intention; however, familiarity has no impact on the relationship between informational social influence and continuance intention.

(1) Objective: In this study, a regression-based multi-modal deep learning model was developed for use in bone age assessment (BAA) utilizing hand radiographic images and clinical data, including patient gender and chronological age, as input data. (2) Methods: A dataset of hand radiographic images from 2974 pediatric patients was used to develop a regression-based multi-modal BAA model. This model integrates hand radiographs using EfficientNetV2S convolutional neural networks (CNNs) and clinical data (gender and chronological age) processed by a simple deep neural network (DNN). This approach enhances the model’s robustness and diagnostic precision, addressing challenges related to imbalanced data distribution and limited sample sizes. (3) Results: The model exhibited good performance on BAA, with an overall mean absolute error (MAE) of 0.410, root mean square error (RMSE) of 0.637, and accuracy of 91.1%. Subgroup analysis revealed higher accuracy in females ≤ 11 years (MAE: 0.267, RMSE: 0.453, accuracy: 95.0%) and >11 years (MAE: 0.402, RMSE: 0.634, accuracy 92.4%) compared to males ≤ 13 years (MAE: 0.665, RMSE: 0.912, accuracy: 79.7%) and >13 years (MAE: 0.647, RMSE: 1.302, accuracy: 84.6%). (4) Conclusion: This model showed a generally good performance on BAA, showing a better performance in female pediatrics compared to male pediatrics and an especially robust performance in female pediatrics ≤ 11 years.