Objective:Urinary calculi are characterized by a high recurrence rate,and patients’adherence to self-management after discharge directly affects health outcomes.Traditional offline follow-up models often face problem...Objective:Urinary calculi are characterized by a high recurrence rate,and patients’adherence to self-management after discharge directly affects health outcomes.Traditional offline follow-up models often face problems such as poor compliance and uneven allocation of medical resources,making it difficult to meet individualized health management needs.Remote follow-up provides a novel solution to optimize long-term management,improve health literacy,and enhance clinical outcomes.This study aims to evaluate the effect of remote follow-up under an intelligent medical collaborative model on quality of life and health-promoting lifestyle in patients with urinary calculi,and to assess its short-term impact on clinical outcomes.Methods:A total of 118 patients with urinary calculi admitted to a tertiary hospital in Hunan Province between August and November 2024 were recruited and randomly assigned to a control group(n=59)or an intervention group(n=59).The control group received routine departmental follow-up,while the intervention group underwent remote follow-up based on an intelligent medical collaborative model for one month.Assessments were conducted before discharge(T0),15 days after discharge(T1),and one month after discharge(T2),using the Wisconsin Stone Quality of Life Questionnaire and the Health-Promoting Lifestyle Profile.At T2,the incidence of forgotten ureteral stents(FUS),ureteral stent-related complications,unplanned readmissions,and patient satisfaction were evaluated.Results:No significant differences were observed between groups at T0 in baseline characteristics or outcome measures(all P>0.05).At T1 and T2,the intervention group had significantly higher health-related quality of life scores than the control group(P<0.05).Generalized estimating equation(GEE)analysis showed significant between-group effects(Wald's χ^(2)=22.961,P<0.001),time effects(Wald's χ^(2)=23.065,P<0.001),and interaction effects(Wald's χ^(2)=6.930,P<0.05).Similarly,at T1 and T2,the intervention group scored significantly higher on health-promoting lifestyle than the control group(P<0.05),with significant between-group effects(Wald's χ^(2)=22.936,P<0.001),time effects(Wald's χ^(2)=10.694,P<0.001),and interaction effects(Wald's χ^(2)=18.921,P<0.05).No significant differences were found between groups in the incidence of FUS,ureteral stent-related complications,or unplanned readmissions(all P>0.05).Patient satisfaction was significantly higher in the intervention group(t=4.089,P<0.001).Conclusion:Remote follow-up under an intelligent medical collaborative model helps improve quality of life,promote health-oriented lifestyles,and enhance patient satisfaction among individuals with urinary calculi.展开更多
Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Metho...Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.展开更多
基金supported by the Innovation Platform’s Open Foundation of Education Department of Hunan Province(18K004)the Natural Science Foundation of Hunan Province(2025JJ50508),China.
文摘Objective:Urinary calculi are characterized by a high recurrence rate,and patients’adherence to self-management after discharge directly affects health outcomes.Traditional offline follow-up models often face problems such as poor compliance and uneven allocation of medical resources,making it difficult to meet individualized health management needs.Remote follow-up provides a novel solution to optimize long-term management,improve health literacy,and enhance clinical outcomes.This study aims to evaluate the effect of remote follow-up under an intelligent medical collaborative model on quality of life and health-promoting lifestyle in patients with urinary calculi,and to assess its short-term impact on clinical outcomes.Methods:A total of 118 patients with urinary calculi admitted to a tertiary hospital in Hunan Province between August and November 2024 were recruited and randomly assigned to a control group(n=59)or an intervention group(n=59).The control group received routine departmental follow-up,while the intervention group underwent remote follow-up based on an intelligent medical collaborative model for one month.Assessments were conducted before discharge(T0),15 days after discharge(T1),and one month after discharge(T2),using the Wisconsin Stone Quality of Life Questionnaire and the Health-Promoting Lifestyle Profile.At T2,the incidence of forgotten ureteral stents(FUS),ureteral stent-related complications,unplanned readmissions,and patient satisfaction were evaluated.Results:No significant differences were observed between groups at T0 in baseline characteristics or outcome measures(all P>0.05).At T1 and T2,the intervention group had significantly higher health-related quality of life scores than the control group(P<0.05).Generalized estimating equation(GEE)analysis showed significant between-group effects(Wald's χ^(2)=22.961,P<0.001),time effects(Wald's χ^(2)=23.065,P<0.001),and interaction effects(Wald's χ^(2)=6.930,P<0.05).Similarly,at T1 and T2,the intervention group scored significantly higher on health-promoting lifestyle than the control group(P<0.05),with significant between-group effects(Wald's χ^(2)=22.936,P<0.001),time effects(Wald's χ^(2)=10.694,P<0.001),and interaction effects(Wald's χ^(2)=18.921,P<0.05).No significant differences were found between groups in the incidence of FUS,ureteral stent-related complications,or unplanned readmissions(all P>0.05).Patient satisfaction was significantly higher in the intervention group(t=4.089,P<0.001).Conclusion:Remote follow-up under an intelligent medical collaborative model helps improve quality of life,promote health-oriented lifestyles,and enhance patient satisfaction among individuals with urinary calculi.
文摘Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.
