Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.

Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supervised networks based on MRI images. In this retrospective study, data were from the osteoarthritis initiative (OAI). The MR images were reconstructed using a sagittal intermediate-weighted fat-suppressed turbo spin-echo sequence. (1) We used 130 knees from the OAI to develop the LGSA-UNet model which fuses the features of adjacent slices and adjusts the blocks in Siam to enable the central slice to obtain rich contextual information. (2) One thousand seven hundred and fifty-six knees from the OAI were included to establish segmentation and classification models. The segmentation model achieved a DICE coefficient ranging from 0.84 to 0.93. The AUC values ranged from 0.85 to 0.95 in the binary models. The accuracy for the three types of menisci (normal, tear, and maceration) ranged from 0.60 to 0.88. Furthermore, 206 knees from the orthopedic hospital were used as an external validation data set to evaluate the performance of the model. The segmentation and classification models still performed well on the external validation set. To compare the diagnostic performances between the deep learning (DL) models and radiologists, the external validation sets were sent to two radiologists. The binary classification model outperformed the diagnostic performance of the junior radiologist (0.82-0.87 versus 0.74-0.88). This study highlights the potential of DL in knee meniscus segmentation and injury classification which can help improve diagnostic efficiency.

Comprehensive evaluation of drought tolerance of six Chinese chestnut varieties (clones) based on flavonoids and other physiological indexes.

Flavonoids are crucial secondary metabolites that possess the ability to mitigate UV damage and withstand both biotic and abiotic stresses. Therefore, it is of immense significance to investigate the flavonoid content as a pivotal indicator for a comprehensive assessment of chestnut's drought tolerance. This study aimed to determine the flavonoid content and drought tolerance-related physiological and biochemical indices of six chestnut varieties (clones) grafted trees-Qianxi 42 (QX42), Qinglong 45 (QL45), Yanshanzaofeng (YSZF), Yanzi (YZ), Yanqiu (YQ), and Yanlong (YL)-under natural drought stress. The results were used to comprehensively analyze the drought tolerance ability of these varieties. The study revealed that the ranking of drought tolerance indices in terms of their ability to reflect drought tolerance was as follows: superoxide (oxide) dismutase (SOD) activity, ascorbate peroxidase (APX) activity, flavone content, catalase (CAT) activity, proline (PRO) content, soluble sugar content, peroxidase (POD) activity, betaine content, flavonol content, hydrogen peroxide (H2O2) content, soluble protein content, superoxide ion (OFR) content, superoxide (ion OFR) production rate, malondialdehyde (MDA) content, chlorophyll content. Through principal component analysis, the contents of flavonoids and flavonols can be used as indicators for comprehensive evaluation of drought tolerance of chestnut. The comprehensive evaluation order of drought tolerance of grafted trees of 6 chestnut varieties (Clones) was: QL45 > QX42 > YQ > YZ > YSZF > YL.

Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images.

BACKGROUND: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability. PURPOSE: To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome. STUDY TYPE: Retrospective. POPULATION: 194 OA progressors (mean age, 62 ± 9 years) and 406 controls (mean age, 61 ± 9 years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts. FIELD STRENGTH/SEQUENCE: Sagittal 2D IW-TSE-FS (IW) and 3D-DESS-WE (DESS) at 3T. ASSESSMENT: Anatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12-month, and 24-month were automatically segmented and served as inputs to form compartment-based graphs for a TRGCN model, which containing both regional and temporal information. The performance of models based on (i) clinical variables alone, (ii) radiologist score alone, (iii) combined features (containing i and ii), (iv) composite TRGCN (combining TRGCN, i and ii), (v) radiomics features, (vi) convolutional neural network based on Densenet-169 were compared. STATISTICAL TESTS: DeLong test was performed to compare the areas under the ROC curve (AUC) of all models. Additionally, interpretability analysis was done to evaluate the contributions of individual regions. A P value <0.05 was considered significant. RESULTS: The composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all P < 0.05). Interpretability analysis highlighted cartilage's importance over other structures (42%-45%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%-67% vs. 12%-37%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12-month: 52% vs. 39%, 24-month: 31% vs. 48%). DATA CONCLUSION: The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression. TECHNICAL EFFICACY: Stage 2.