Citations

Abstract Objective: This study aimed to develop a machine learning model for the diagnosis of chronic renal failure (CRF) with spleen–kidney qi deficiency (SKQD) syndrome by investigating facial chromatic biomarkers based on traditional Chinese medicine (TCM) theory. Materials and Methods: This cross-sectional study analyzed TCM-defined facial regions in 303 patients with CRF (166 SKQD and 137 non-SKQD) and 25 healthy controls using active appearance models and RGB/Lab color spaces. Key chromatic features were selected using Chi-square automatic interaction detection (CHAID) decision trees to construct a multibranch deep convolutional neural network (CNN), nomogram, and hybrid model. The model performances were compared using the DeLong test. Results: The SKQD group showed a higher glomerular filtration rate, albumin level, and facial R-value with a lower serum creatinine level (all P < 0.05), accompanied by a higher prevalence of shortness of breath with reluctance to speak, and cold extremities ( P < 0.05) compared with the non-SKQD group. Chromatic analysis revealed elevated R/a values in the kidney region using the natural facial color region method ( P < 0.05), and distinctively higher R -values in the bladder-uterus region and a -values in the right small intestine region using the Mingtang facial color region method ( P < 0.05). The CHAID algorithm selected four diagnostic determinants, including the overall facial R -values. The CNN achieved superior discriminative performance (test area under the receiver operating characteristic curve = 0.74 vs. nomogram 0.68, DeLong test P < 0.001) with no added benefit from model integration ( P > 0.05). Conclusions: Facial chromatic biomarkers effectively differentiated SKQD in patients with CRF when analyzed using TCM-defined regions. The superior performance of the CNN validates AI-driven TCM diagnostics as a clinically actionable tool for precise syndrome classification.

ABSTRACTBackgroundPercutaneous transluminal angioplasty and stenting (PTAS) in patients with carotid stenosis may have potential effects on brain parenchyma. However, current studies on parenchymal changes are scarce due to the need for advanced imaging modalities. Consequently, the alterations in brain parenchyma following PTAS remain an unsolved issue.PurposeTo investigate changes to the brain parenchyma using magnetic resonance elastography (MRE).Study TypeProspective.Population13 patients (6 women and 7 men; 39 MRI imaging sessions) with severe unilateral carotid stenosis patients indicated for PTAS were recruited between 2021 and 2024.Field Strength/SequenceNoncontrast MRI sequences including MRE (spin echo) were acquired using 3 T scanners.AssessmentAll patients underwent MRE before (preprocedural), within 24 h (early postprocedural) and 3 months after (delayed postprocedural) PTAS. Preprocedural and delayed postprocedural ultrasonographic peak systolic velocity (PSV) was recorded. MRE stiffness and damping ratio were evaluated via neural network inversion of the whole brain, in 14 gray matter (GM) and 12 white matter (WM) regions.Statistical TestsStiffness and damping ratio differences between each pair of MR sessions for each subject were identified by paired sample t tests. The correlations of stiffness and damping ratio with stenosis grade and ultrasonographic PSV dynamics were evaluated by Pearson correlation coefficients. The statistical significance was defined as p < 0.05.ResultsThe stiffness of lesion side insula, deep GM, and deep WM increased significantly from preprocedural to delayed postprocedural MRE. Increasing deep GM stiffness on the lesion side was positively correlated with the DSA stenosis grade significantly (r = 0.609). The lesion side insula stiffness increments were positively correlated with PSV decrements significantly (r = 0.664).Data ConclusionRegional brain stiffness increased 3 months after PTAS. Lesion side stiffness was positively correlated with stenosis grades in deep GM and PSV decrements in the insula.Evidence Level2.Technical EfficacyStage 2.