Jun Du

Background Osteoporosis progresses through stages characterized by declining bone mineral density, vertebral deterioration, and muscle atrophy, with bone-muscle interactions driving synergistic degeneration. Methods This study retrospectively collected data from 444 patients aged 50 and older, who underwent DXA, CT, and MRI scans at the First Affiliated Hospital of Soochow University. CT values were measured for 6 vertebrae (L1-S1) and 30 adjacent muscle groups (psoas major, erector spinae, quadratus lumborum) to assess vertebral and muscle density. After analyzing changes in CT values across osteoporosis stages development to capture vertebrae and muscles degeneration pattern, we use multiple interpretable machine learning models to construct classification model and construct bone-muscle interaction network. Results This study found that osteoporosis progresses with age, with faster degeneration in females. Early stages show significant bone degradation, especially in L5 and S1 vertebrae, while later stages highlight muscle atrophy. Machine learning models, enhanced by Recursive Feature Elimination (RFE), effectively predicted disease progression (with Normal vs. Osteopenia 0.788, Normal vs. Osteoporosis 0.909, Normal vs. Osteoporotic fracture 0.942, Osteopenia vs. Osteoporosis 0.708, Osteopenia vs. Osteoporotic fracture 0.820 and Osteoporosis vs. Osteoporotic fracture 0.770). The Combined bone muscle interaction network reveals that vertebrae dominate early interactions, shifting to the muscle-clustered module in advanced stages, reflecting the complex degeneration of both bone and muscle. Conclusion This study develops classification models and analyze bone-muscle interactions in osteoporosis, uncovering synergistic degradation patterns across disease stages. The innovative BMINet toolkit offers an efficient, interpretable framework for personalized analysis, advancing precision medicine and integrated care for osteoporosis patients.

To evaluate the diagnostic accuracy of a new modified MR dual precision positioning of thin-slice oblique sagittal fat suppression proton density-weighted imaging (DPP-TSO-Sag-FS-PDWI) sequence in detecting ACL injuries and its grades compared to standard sequences using arthroscopy as the standard reference. 42 patients enrolled in this retrospective study received the 1.5-T MRI with standard sequences and the new modified DPP-TSO-Sag-FS-PDWI sequence, and their arthroscopy results was recorded. The Mc Nemer-Bowker and weighted Kappa was performed to compare the consistency of MRI diagnosis with arthroscopic results. Finally, the diagnostic accuracy was calculated based on the true positive, true negative, false negative and false positive values. The diagnostic consistency of the DPP-TSO-Sag-FS-PDWI were higher than standard sequences for both reader 1 (K = 0.876 vs. 0.620) and reader 2 (K = 0.833 vs. 0.683) with good diagnostic repeatability (K = 0.794 vs. 0.598). Furthermore, the DPP-TSO-Sag-FS-PDWI can classify and diagnose three grades of ACL injury [the sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value were more than 84%], especially for grade II injury as the PPV was superior for reader 1 (92.3% vs. 53.9%) and reader 2 (84.6% vs. 69.2%). The new modified DPP-TSO-Sag-FS-PDWI sequence can display the ACL injury on one or continuous levels by maximizing the acquisition of complete ligament shape and true anatomical images, and excluding the influence of anatomical differences between individuals. It can improve the diagnostic accuracy with good repeatability and classify three grades of the ACL injury.

Background: It is still uncertain to determine the exact diagnostic accuracy of MRI for medial meniscus (MM) tear, lateral meniscus (LM) tear and MM posterior root tear (MMPRT) at different magnetic field intensities (MFIs), different sequences and different publication dates. This study aimed to identify the diagnostic performance of MRI for different meniscus lesions at different MFIs, different sequences and different publication dates, and also to compare it with physical examination. Methods: PubMed, Embase, Ovid database, Biosis Previews, Cochrane library, Web of Sciences and manual searching were performed from 1 January 2000 to 31 December 2021. Prospective studies of meniscus injuries examined by physical examination, MRI and arthroscopy were included. Results: Thirteen studies with 1583 meniscal tears were included. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR) and area under curve (AUC) were 87% (95% confidence interval (CI), 86-89%), 82% (80-83%), 7.44 (5.11- 10.75), 0.18 (0.14-0.23), 45.95 (26,68-79.15) and 0.93, respectively. The pooled specificity between MM and LM (p = 0.015), the pooled sensitivity and LR- between MM and MMPR (p = 0.031), different MRI sequences (p = 0.035, p = 0.027), and the accuracy of less than 1.5T and 3.0T (p = 0.04), 1.5T and 3.0T (p = 0.035) were statistically different. There was no publication bias (p = 0.54). Conclusion: MRI performed well in the diagnosis of MM tear, LM tear and MMPRT, and the diagnostic performance of physical examination is similar to MRI. The diagnostic accuracy of 3.0T is the highest, and the -weighted imaging (SWI) sequence may be beneficial for diagnosing meniscus tear. However, there are not enough evidence to prove that recent studies are significantly better than previous ones.

Purpose To evaluate the diagnostic performance of the transfer learning approach for grading diagnosis of ACL injury on a new modified dual precision positioning of thin-slice oblique sagittal FS-PDWI (DPP-TSO-Sag-FS-PDWI) sequence. And compare the prediction performances between artificial intelligence (AI) and radiologists. Materials and Methods Patients with both DPP-TSO-Sag-FS-PDWI sequence and arthroscopic results were included. We performed a transfer learning approach using the pre-trained EfficientNet-B0 model, including whole image and regions of interest (ROI) image inputs, and reset its parameters to achieve an automatic hierarchical diagnosis of ACL. Results A total of 235 patients (145 men and 90 women, 37.91 ± 14.77 years) with 665 images were analyzed. The consistencies of AI and arthroscopy (Kappa value > 0.94), radiologists and arthroscopy (Kappa value > 0.83, p = 0.000) were almost perfect. No statistical difference exists between the whole image and radiologists in the diagnosis of normal ACL (p = 0.063) and grade 3 injury (p = 1.000), while the whole image was better than radiologists in grade 1 (p = 0.012) and grade 2 injury (p = 0.003). Conclusion The transfer learning approach exhibits its feasibility in the diagnosis of ACL injury based on the new modified MR DPP-TSO-Sag-FS-PDWI sequence, suggesting that it can help radiologists hierarchical diagnose ACL injuries, especially grade 2 injury.

Background: Medical devices are used in almost all orthopedic surgical subspecialties, and the frequency of adverse events is increasing, which should not be ignored. To provide suggestions on how to avoid implant recalls from the perspective of manufacturers, medical institutions and supervisions, as well as how to respond promptly to adverse events. Methods: The research extracted recalls of osteosynthesis implants and joint replacement implants from January 1, 2011, to June 30, 2021, in the CNMPA, FDA, HC and ATGA websites and collected the information on device name, recall time, recall class, recall manufacturer, device classification and affected areas. Moreover, the McKinsey 7S model and fishbone diagram were used to analyze recall reasons. Results: A total of 315 cases of osteosynthesis implants and 286 cases of joint replacement implants were reported in China, the USA, Canada and Australia. The recalls number from 2016 to 2021 was more than that from 2011 to 2015 for osteosynthesis implant (p = 0.012) and joint replacement implant (p = 0.002), and both mainly focused on class II (76.19% and 78.32%). There were statistical differences in the four countries for both implants (p = 0.000), especially osteosynthesis implant between China and the USA (p = 0.000), China and Canada (p = 0.001), the USA and Australia (p = 0.002), and joint replacement implant between China and Australia (p = 0.000). Conclusions: To avoid the recalls of such implants, manufacturers should strictly select implant materials and components, develop detailed labels and instructions, severely control the packaging process and establish the integrity of medical device data. Medical institutions should standardize procurement procedures, use qualified equipment and train medical workers. It also requires supervisions to conduct premarket safety assessments. In addition, regulators should strengthen supervision and establish reporting systems to deal with the occurrence of adverse events promptly.

Background Medical devices are used in almost all orthopedic surgical subspecialties, and the frequency of adverse events is increasing, which should not be ignored. To provide suggestions on how to avoid implant recalls from the perspective of manufacturers, medical institutions and supervisions, as well as how to respond promptly to adverse events.Methods The research extracted recalls of osteosynthesis implants and Joint replacement implants from January 1, 2011 to June 30, 2021 in the CNMPA, FDA, HC and ATGA websites, and collected the information on device name, recall time, recall class, recall manufacturer, device classification and affected areas. Moreover, the McKinsey 7S model and fishbone diagram were used to analyze recall reasons.Results A total of 315 cases of osteosynthesis implants and 286 cases of Joint replacement implants were reported in China, the United States, Canada and Australia. The recall number of the two implants from 2016 to 2021 were more than that from 2011 to 2015 (p = 0.012, p = 0.002) and the recall class of the two implants mainly focused on class II (76.19% and 78.32%). The affected areas were almost all over the world, mainly concentrated in developed countries. The recall reasons for both implants were similar, with process control, packaging process control, mislabeled and nonconforming materials or components for the most. But the main reasons of the two implants differed among four countries (p = 0.000).Conclusions To avoid the recalls of such implants, manufacturers should strictly select implant materials and components, develop detailed labels and instructions, severely control the packaging process, and establish the integrity of medical device data. Medical institutions should standardize procurement procedures, use qualified equipment and train medical workers. It also requires supervisions to conduct premarket safety assessments. In addition, Regulators should strengthen supervision and establish reporting systems to deal with the occurrence of adverse events promptly.

Preoperative identification of intramuscular hemangioma (IMH) subtypes (capillary hemangioma, cavernous hemangioma, and mixed hemangioma) is urgently necessary. Enhanced T2*-weighted angiography (ESWAN) is sensitive to vessels and metabolites and can be used to diagnose IMH subtypes. To compare the diagnostic performances of ESWAN and conventional magnetic resonance imaging (MRI) for qualitative and quantitative diagnosis of IMH subtypes. In total, 23 patients with IMHs were examined using conventional MRI and ESWAN. The signal intensity ratios (SIRs) of conventional MRI and ESWAN were measured. There was no significant difference for volume among the three subtypes (P = 0.124, P = 0.145). Various shapes and MRI signals were shown in the three subtypes of IMH. There was no significant difference for SIRs of conventional MRI (P = 0.558, P = 0.259, P = 0.385, P = 0.347). However, there was a significant difference for SIRs of ESWAN parameters (P = 0.050, P < 0.001, P = 0.005, P = 0.002). Capillary hemangiomas can be diagnosed when R2* SIR is 35.226%, and phase SIR >2.536. In addition, mixed hemangiomas should be considered when T2* SIR is >0.662 and R2* SIR <1.618. Conventional MRI can only display the volume, shape, and signal of IMHs. 3D-MinIP imaging of ESWAN can show the veins and minor hemorrhage. SIRs of ESWAN parameters including T2* value, R2* value, phase value, and percentage of ITSS can be used to quantitatively diagnose capillary hemangiomas, cavernous hemangiomas, and mixed hemangiomas.

Purpose To evaluate the diagnostic accuracy of a new modified MR dual precision positioning of thin-slice oblique sagittal fat suppression proton density-weighted imaging (DPP-TSO-Sag-FS-PDWI) sequence in detecting ACL injuries and its grades compared to standard sequences using arthroscopy as the standard reference.Materials and Methods 42 patients enrolled in this retrospective study received the 1.5-T MRI with standard sequences and the new modified DPP-TSO-Sag-FS-PDWI sequence, and their arthroscopy results was recorded. The Mc Nemer-Bowker and weighted Kappa was performed to compare the consistency of MRI diagnosis with arthroscopic results. Finally, the diagnostic accuracy was calculated based on the true positive, true negative, false negative and false positive values.Results The diagnostic consistency of the DPP-TSO-Sag-FS-PDWI were higher than standard sequences for both reader 1 (K = 0.876 vs. 0.620) and reader 2 (K = 0.833 vs. 0.683) with good diagnostic repeatability (K = 0.794 vs. 0.598). Furthermore, the DPP-TSO-Sag-FS-PDWI can classify and diagnose three grades of ACL injury [the sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value were more than 84%], especially for grade II injury as the PPV was superior for reader 1 (92.3% vs. 53.9%) and reader 2 (84.6% vs. 69.2%).Conclusion The new modified DPP-TSO-Sag-FS-PDWI sequence can display the ACL injury on one or continuous levels by maximizing the acquisition of complete ligament shape and true anatomical images, and excluding the influence of anatomical differences between individuals. It can improve the diagnostic accuracy with good repeatability and classify three grades of the ACL injury.