Artificial Intelligence Needs Clinical Intelligence to Succeed

AI hybrid survival assessment for advanced heart failure patients with renal dysfunction

Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Many prognosis assessments are hindered by researcher biases, redundant predictors, and lack of clinical applicability. In this study, we enroll 1736 AHF/RD patients, including data from Henan Province Clinical Research Center for Cardiovascular Diseases (which encompasses 11 hospital subcenters), and Beth Israel Deaconess Medical Center. We developed an AI hybrid modeling framework, assembling 12 learners with different feature selection paradigms to expand modeling schemes. The optimized strategy is identified from 132 potential schemes to establish an explainable survival assessment system: AIHFLevel. The conditional inference survival tree determines a probability threshold for prognostic stratification. The evaluation confirmed the system's robustness in discrimination, calibration, generalization, and clinical implications. AIHFLevel outperforms existing models, clinical features, and biomarkers. We also launch an open and user-friendly website www.hf-ai-survival.com , empowering healthcare professionals with enhanced tools for continuous risk monitoring and precise risk profiling.

Development and Validation of an Artificial Intelligence Preoperative Planning and Patient-Specific Instrumentation System for Total Knee Arthroplasty

BACKGROUND

Accurate preoperative planning for total knee arthroplasty (TKA) is crucial. Computed tomography (CT)-based preoperative planning offers more comprehensive information and can also be used to design patient-specific instrumentation (PSI), but it requires well-reconstructed and segmented images, and the process is complex and time-consuming. This study aimed to develop an artificial intelligence (AI) preoperative planning and PSI system for TKA and to validate its time savings and accuracy in clinical applications.

METHODS

The 3D-UNet and modified HRNet neural network structures were used to develop the AI preoperative planning and PSI system (AIJOINT). Forty-two patients who were scheduled for TKA underwent both AI and manual CT processing and planning for component sizing, 20 of whom had their PSIs designed and applied intraoperatively. The time consumed and the size and orientation of the postoperative component were recorded.

RESULTS

The Dice similarity coefficient (DSC) and loss function indicated excellent performance of the neural network structure in CT image segmentation. AIJOINT was faster than conventional methods for CT segmentation (3.74 ± 0.82 vs. 128.88 ± 17.31 min, p < 0.05) and PSI design (35.10 ± 3.98 vs. 159.52 ± 17.14 min, p < 0.05) without increasing the time for size planning. The accuracy of AIJOINT in planning the size of both femoral and tibial components was 92.9%, while the accuracy of the conventional method in planning the size of the femoral and tibial components was 42.9% and 47.6%, respectively (p < 0.05). In addition, AI-based PSI improved the accuracy of the hip-knee-ankle angle and reduced postoperative blood loss (p < 0.05).

CONCLUSION

AIJOINT significantly reduces the time needed for CT processing and PSI design without increasing the time for size planning, accurately predicts the component size, and improves the accuracy of lower limb alignment in TKA patients, providing a meaningful supplement to the application of AI in orthopaedics.

Hospitalization for Heart Failure in the United States, UK, Taiwan, and Japan: An International Comparison of Administrative Health Records on 413,385 Individual Patients

BACKGROUND

Registries show international variations in the characteristics and outcome of patients with heart failure (HF), but national samples are rarely large, and case selection may be biased owing to enrolment in academic centers. National administrative datasets provide large samples with a low risk of bias. In this study, we compared the characteristics, health care resource use (HRU) and outcomes of patients with primary HF hospitalizations (HFH) using electronic health records (EHR) from 4 high-income countries (United States, UK, Taiwan, Japan) on 3 continents.

METHODS AND RESULTS

We used electronic health record to identify unplanned HFH between 2012 and 2014. We identified 231,512, 10,991, 36,900, and 133,982 patients with a primary HFH from the United States, the UK, Taiwan, and Japan, respectively. HFH per 100,000 population was highest in the United States and lowest in Taiwan. Fewer patients in Taiwan and Japan were obese or had chronic kidney disease. The length of hospital stay was shortest in the United States (median 4 days) and longer in the UK, Taiwan, and Japan (medians of 7, 9, and 17 days, respectively). HRU during hospitalization was highest in Japan and lowest in UK. Crude and direct standardized in-hospital mortality was lowest in the United States (direct standardized rates 1.8, 95% confidence interval 1.7%-1.9%) and progressively higher in Taiwan (direct standardized rates 3.9, 95% CI 3.8%-4.1%), the UK (direct standardized rates 6.4, 95% CI 6.1%-6.7%), and Japan (direct standardized rates 6.7, 95% CI 6.6%-6.8%). The 30-day all-cause (25.8%) and HF (7.2%) readmissions were highest in the United States and lowest in Japan (11.9% and 5.1%, respectively).

CONCLUSIONS

Marked international variations in patient characteristics, HRU, and clinical outcomes exist; understanding them might inform health care policy and international trial design.