Automatic measurement of the patellofemoral joint parameters in the Laurin view: a deep learning-based approach.

OBJECTIVES: To explore the performance of a deep learning-based algorithm for automatic patellofemoral joint (PFJ) parameter measurements from the Laurin view. METHODS: A total of 1431 consecutive Laurin views of the PFJ were retrospectively collected and divided into two parts: (1) the model development dataset (dataset 1, n = 1230) and (2) the hold-out test set (dataset 2, n = 201). Dataset 1 was used to develop the U-shaped fully convolutional network (U-Net) model to segment the landmarks of the PFJ. Based on the predicted landmarks, the PFJ parameters were calculated, including the sulcus angle (SA), congruence angle (CA), patellofemoral ratio (PFR), and lateral patellar tilt (LPT). Dataset 2 was used to assess the model performance. The mean of three radiologists who independently measured the PFJ parameters was defined as the reference standard. Model performance was assessed by the intraclass correlation coefficient (ICC), mean absolute difference (MAD), and root mean square (RMS) compared to the reference standard. Ninety-five percent limits of agreement (95% LoA) were calculated pairwise for each radiologist, reference standard, and model. RESULTS: Compared with the reference standard, U-Net showed good performance for predicting SA, CA, PFR, and LPT, with ICC = 0.85-0.97, MAD = 0.06-5.09, and RMS = 0.09-6.90 in the hold-out test set. Except for the PFR, the remaining parameters measured between the reference standard and the model were within the 95% LoA in the hold-out test dataset. CONCLUSIONS: The U-Net-based deep learning approach had a relatively high model performance in automatically measuring SA, CA, PFR, and LPT. KEY POINTS: • The U-Net model could be used to segment the landmarks of the PFJ and calculate the SA, CA, PFR, and LPT, which could be used to evaluate the patellar instability. • In the hold-out test, the automatic measurement model yielded comparable performance with reference standard. • The automatic measurement model could still accurately predict SA, CA, PFR, and LPT in patients with PI and/or PFOA.

Automatic diagnosis and grading of patellofemoral osteoarthritis from the axial radiographic view: a deep learning-based approach.

BACKGROUND: Patellofemoral osteoarthritis (PFOA) has a high prevalence and is assessed on axial radiography of the patellofemoral joint (PFJ). A deep learning (DL)-based approach could help radiologists automatically diagnose and grade PFOA via interpreting axial radiographs. PURPOSE: To develop and assess the performance of a DL-based approach for diagnosing and grading PFOA on axial radiographs. MATERIAL AND METHODS: A total of 1280 (dataset 1) axial radiographs were retrospectively collected and utilized to develop the high-resolution network (HRNet)-based classification models. The ground truth was the interpretation from two experienced radiologists in consensus according to the K-L grading system. A binary-class model was trained to diagnose the presence (K-L 2∼4) or absence (K-L 0∼1) of PFOA. A multi-class model was used to grade the stage of PFOA, i.e. from K-L 0 to K-L 4. Model performances were evaluated using the receiver operating characteristics (ROC), confusion matrix, and the corresponding evaluation metrics (positive predictive value [PPV], negative predictive value [NPV], F1 score, sensitivity, specificity, accuracy) of the internal test set (n = 129) from dataset 1 and an external validation set (dataset 2, n = 187). RESULTS: For the binary-class model, the area under the curve (AUC) was 0.91 in the internal test set and 0.90 in the external validation set. For grading PFOA, moderate to severe stage of PFOA exhibited a good performance in these two datasets (AUC = 0.91-0.98, PPV = 0.69-0.90, NPV = 0.92-0.99, F1 score = 0.72-0.87, sensitivity = 0.75-0.87, specificity = 0.90-0.99, accuracy = 0.87-0.98). CONCLUSION: The HRNet-based approach performed well in diagnosing and grading radiographic PFOA, especially for the moderate to severe cases.

Expression of selenium-independent glutathione peroxidase 5 (GPx5) in the epididymis of Small Tail Han sheep.

OBJECTIVE: Selenium-independent glutathione peroxidase (GPx5) is specifically expressed in the mammalian epididymis and plays an important role in protecting sperm from reactive oxygen species and lipid peroxidation damage. This study investigates GPx5 expression in the epididymis of Small Tail Han sheep. METHODS: GPx5 expression was studied in three age groups: lamb (2 to 3 months), young (8 to 10 months), and adult (18 to 24 months). The epididymis of each age group divided into caput, corpus and cauda, respectively. Analysis the expression quantity of GPx5 in epididymis and testis by real-time fluorescent quantitative polymerase chain reaction and Western blot. Finally, GPx5 protein locating in the epididymis by immunohistochemical. RESULTS: The results demonstrate that in the lamb group, the GPx5 mRNA, but not protein, can be detected. GPx5 mRNA and expressed protein were detected in both the young and adult groups. Moreover, both the mRNA and protein levels of GPx5 were significantly higher in the young group than in other two groups. When the different segments of epididymis were investigated, GPx5 mRNA was expressed in each segment of epididymis regardless of age. Additionally, the mRNA level in the caput was significantly higher than that in corpus and cauda within same age group. The GPx5 protein was in the epithelial cells' cytoplasm. However, GPx5 mRNA and protein were not detected in the testis. CONCLUSION: These results suggest that GPx5 is mainly expressed in the epididymis of Small Tail Han sheep, and that the expression level of GPx5 is associated with age. Additionally, GPx5 was primarily expressed in the epithelial cells of the caput. Taken together, these studies indicate that GPx5 is expressed in the epididymis in all age grades.

Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning.

BACKGROUND: Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three-dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor-intensive procedures and inherent variability. In the realm of medical image analysis, deep learning has led to a paradigm shift, yet the development of a comprehensive automated tool for the precise segmentation and measurement of ureters in MR images is an unaddressed challenge. PURPOSE: The ureter was quantitatively measured on 3D MRU images using a deep learning model. METHODS: A retrospective cohort of 445 3D MRU scans (443 patients, 52 ± 18 years; 217 female patients) was collected and split into training, validation, and internal testing cohorts. A 3D V-Net model was trained for urinary tract segmentation, and a post-processing algorithm was developed for ureteral measurements. The accuracy of the segmentation was evaluated using the Dice similarity coefficient (DSC) and volume intraclass correlation coefficient (ICC), with ground truth segmentations provided by experienced radiologists. The external cohort comprised 50 scans (50 patients, 55 ± 21 years; 30 female patients), and the model-predicted ureteral diameter measurements were compared with manual measurements to assess system performance. The various diameter parameters of ureter among the different measurement methods (ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction) were assessed with Friedman tests and post hoc Dunn test. The effectiveness of the UTO diagnosis was assessed by receiver operating characteristic (ROC) curves and their respective areas under the curve (AUC) between different methods. RESULTS: In both the internal test and external cohorts, the mean DSC values for bilateral ureters exceeded 0.70. The ICCs for the bilateral ureter volume obtained by comparing the model and manual segmentation were all greater than 0.96 (p  < â€¯0.05), except for the right ureter in the internal test cohort, for which the ICC was 0.773 (p  < â€¯0.05). The mean DSCs for interobserver and intraobserver reliability were all above 0.97. The maximum diameter of the ureter exhibited no statistically significant differences either in the dilated (p = 0.08) or in the non-dilated (p = 0.32) ureters across the three measurement methods. The AUCs of ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction in diagnosing UTO were 0.988 (95% CI: 0.934, 1.000), 0.961 (95% CI: 0.893, 0.991), and 0.979 (95% CI: 0.919, 0.998), respectively. There was no statistical difference between AUCs of the different methods (p > 0.05). CONCLUSION: The proposed deep learning model and post-processing algorithm provide an effective means for the quantitative evaluation of urinary diseases using 3D MRU images.

The intragenic mRNA-microRNA regulatory network during telogen-anagen hair follicle transition in the cashmere goat.

It is widely accepted that the periodic cycle of hair follicles is controlled by the biological clock, but the molecular regulatory mechanisms of the hair follicle cycle have not been thoroughly studied. The secondary hair follicle of the cashmere goat is characterized by seasonal periodic changes throughout life. In the hair follicle cycle, the initiation of hair follicles is of great significance for hair follicle regeneration. To provide a reference for hair follicle research, our study compared differences in mRNA expression and microRNA expression during the growth and repose stages of cashmere goat skin samples. Through microRNA and mRNA association analysis, we found microRNAs and target genes that play major regulatory roles in hair follicle initiation. We further constructed an mRNA-microRNA interaction network and found that hair follicle initiation and development were related to MiR-195 and the genes CHP1, SMAD2, FZD6 and SIAH1.