Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

Background: This study aimed to assess the severity of postoperative delirium (PD) in elderly patients who underwent reverse total shoulder arthroplasty (rTSA) for irreparable massive rotator cuff tears (mRCTs) under general anesthesia (GA) compared to those under interscalene block (IB). Methods: Forty elderly patients aged 65 years or older diagnosed with an irreparable mRCT who underwent rTSA were included in the prospective case-controlled study. Of these, 20 patients were operated under GA and the other 20 under IB. The average age was 77.1 years (range, 65–95 years). The severity of delirious symptoms was evaluated by the Delirium Rating Scale–revised–98 (DRS) score from the patients or guardians before the surgery and at 0, 3, and 7 days and 1, 3, and 6 months after the surgery and compared between the 2 groups. Results: Immediately after surgery, the visual analog scale score difference between the groups was statistically significant, with the GA group at 6.25 (standard deviation, ± 0.85) and the IB group at 3.80 (± 0.62) (p < 0.001). On the day of operation, the mean DRS score in the GA and IB groups were 9.10 (± 5.63) and 6.60 (± 5.33), respectively (p = 0.157). On day 3 of surgery, the mean DRS score in the GA group peaked to 9.95 (± 8.73), while in the IB group, it declined to 6.40 (±5.81) (p = 0.138). After 3 days, DRS scores showed a decreasing trend in both groups. When comparing the mean change (∆) from the preoperative baseline scores to the postoperative values, the ∆DRS score was significantly higher with 4.15 (± 4.53) points in the GA group as compared to 1.30 (± 1.92) in the IB group (p = 0.014). Conclusions IB can be an attractive and efficient anesthetic choice in preventing PD for elderly patients undergoing rTSA for irreparable mRCTs. The IB group showed lower DRS scores and a peak on day 0 compared to the higher DRS scores and peak on day 3 in the GA group. Additionally, IB showed less pain than GA.

Background: Anterior dislocation in epilepsy patients is relatively severe, difficult to treat, and prone to recurrence. The purpose of this study was to compare the results of arthroscopic Bankart repair and the open Latarjet procedure in epilepsy patients who had anterior shoulder instability and to compare the results of the open Latarjet procedure in epilepsy and non-epileptic groups. Methods: A total of 57 shoulders (34 dominant) in 55 patients (18–50 years, 45 men and 10 women) with anterior glenohumeral instability were included in the study and the average follow-up was 24 months. Out of 21 epilepsy patients (23 shoulders), 11 were treated with the open Latarjet procedure and 12 with arthroscopic Bankart repair. Additionally, comparisons were made between the 34 non-epileptic patients who underwent the open Latarjet procedure and the epilepsy patients who underwent the same procedure. Results: In the epilepsy group, all 12 patients who underwent Bankart repair had on-track lesions, and all 11 patients who underwent the Latarjet procedure had off-track lesions. In the non-epilepsy group, all cases were off-track lesions. In the epilepsy group, there was no significant difference in the postoperative clinical outcome and recurrence rate between the Bankart repair and Latarjet procedure groups. In the Latarjet group, postoperative re-dislocation rate in the non-epilepsy patients was 14% (5/34 cases), compared to 45% (5/11 cases) in the epilepsy patients, 4 of which 4 occurred during seizures. It was 41% in the Bankart repair group for on-track lesions, which was similar to the recurrence rate after the Latarjet for off-track lesions in the epilepsy group. Conclusions After the Latarjet procedure, the functional outcomes in the epilepsy group were similar to those in the non-epilepsy group, except for the higher re-dislocation rate. With either of the surgical procedures, the re-dislocation rate secondary to seizures was very high. Despite the presence of on-track lesions, the Latarjet procedure would be more preferrable for anterior stabilization in epilepsy patients, in view of the high recurrence rate with arthroscopic Bankart repair.

Background: This study aimed to report the short- and midterm outcomes of arthroscopic rotator cuff repair (ARCR) and reverse shoulder arthroplasty (RSA) in weight-bearing shoulders. Methods: This retrospective multicenter study included 19 cases of ARCR and 10 cases of RSA performed in weight-bearing shoulders from 2009 to 2021. In the ARCR group, postoperative 6-month magnetic resonance imaging confirmed the tendon integrity. In the RSA group, scapular notching, acromial fracture, and implant failure were assessed using plain radiographs, and complications were recorded. In both groups, preoperative and postoperative range of motion and functional scores were documented, along with subjective satisfaction and arm use for weight-bearing on the shoulders. For patients followed up for > 5 years, a midterm analysis was performed. Results: The ARCR group included 8 men and 11 women (average age, 58.8 ± 8.0 years). Initially, Patte types 1, 2, and 3 were noted in 9, 8, and 2 patients, respectively, and 4 patients exhibited full-thickness subscapularis tears. Four patients showed supraspinatus retear, and 2 patients showed subscapularis retear. Retear of any rotator cuff was observed in 5 patients (26.3%). Twelve patients were followed up for > 5 years; 11 (91.7%) used their operated arm for weight-bearing and 9 (75.0%) were satisfied. The RSA group included 5 men and 5 women (average age, 74.3 ± 7.9 years). Procedures included RSAs for cuff tear arthropathy (n = 6), osteoarthritis (n = 3), and fracture nonunion (n = 1). No cases of dislocation, prosthesis loosening, or disassociation were observed throughout the follow-up. However, 1 patient required implant removal due to infection, and 4 patients showed stage 1 scapular notching. Five patients were followed up for > 5 years, all of whom expressed satisfaction and used their operated arms for weight-bearing, despite mean forward flexion (107.5° ± 12.6°) and American Shoulder and Elbow Surgeons score (61.5 ± 5.3) being less than reported patient acceptable symptomatic state (110° and 76, respectively). Conclusions Both ARCR and RSA showed promising outcomes in terms of weight-bearing on the operated arm and subjective satisfaction at short- and midterm follow-up. Therefore, neither of these surgeries should be considered contraindicated for patients with weight-bearing shoulder conditions.

Background: and Purpose: This clinical practice guideline provides evidence-based recommendations for treatment of dementia, focusing on cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists for Alzheimer’s disease (AD) and other types of dementia. Methods: Using the Population, Intervention, Comparison, Outcomes (PICO) framework, we developed key clinical questions and conducted systematic literature reviews. A multidisciplinary panel of experts, organized by the Korean Dementia Association, evaluated randomized controlled trials and observational studies. Recommendations were graded for evidence quality and strength using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Results: Three main recommendations are presented: (1) For AD, cholinesterase inhibitors (donepezil, rivastigmine, galantamine) are strongly recommended for improving cognition and daily function based on moderate evidence; (2) Cholinesterase inhibitors are conditionally recommended for vascular dementia and Parkinson’s disease dementia, with a strong recommendation for Lewy body dementia; (3) For moderate to severe AD, NMDA receptor antagonist (memantine) is strongly recommended, demonstrating significant cognitive and functional improvements. Both drug classes showed favorable safety profiles with manageable side effects. Conclusions This guideline offers standardized, evidence-based pharmacologic recommendations for dementia management, with specific guidance on cholinesterase inhibitors and NMDA receptor antagonists. It aims to support clinical decision-making and improve patient outcomes in dementia care. Further updates will address emerging treatments, including amyloid-targeting therapies, to reflect advances in dementia management.

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

Purpose: Pulmonary tumor thrombotic microangiopathy (PTTM) is a fatal complication of gastric cancer (GC). This study aimed to evaluate the clinical characteristics, outcomes, and immunohistochemical profiles of patients with GC-induced PTTM. Materials and Methods: From 2011 to 2023, 8 patients were clinically diagnosed with PTTM associated with GC antemortem. Clinical features and outcomes were reviewed, and immunohistochemical staining for c-erbB-2, MutL protein homolog 1, and programmed cell death ligand-1 was performed. Results: The median patient age was 56 years (range, 34–66 years). In all the patients, the tumors exhibited either ulceroinfiltrative or diffusely infiltrative gross morphology.The median tumor size was 5.8 cm (range, 2.0 cm–15.0 cm). Poorly differentiated adenocarcinoma was the most common histological type (6/8, 75%), followed by signet ring cell carcinoma (1/8, 12.5%) and moderately differentiated adenocarcinoma (1/8, 12.5%).Chest computed tomography revealed ground-glass opacities (7/8, 87.5%) or tree-in-bud signs (2/8, 25.0%) without definite evidence of pulmonary thromboembolism. Disseminated intravascular coagulation was present in 62.5% (5/8) of the patients diagnosed with PTTM.C-erbB-2 was positive in one patient (1/8, 12.5%). One patient who received palliative chemotherapy after developing PTTM survived for 35 days, whereas the other 7 patients who did not receive chemotherapy after developing PTTM survived for 7 days or less after PTTM diagnosis. Conclusions Most patients with GC-induced PTTM had an undifferentiated-type histology, infiltrative morphology, and extremely poor survival. Palliative chemotherapy may benefit patients with GC-induced PTTM; however, further studies are needed to explore the potential of targeted therapy in these patients.

Background: This study aimed to assess the severity of postoperative delirium (PD) in elderly patients who underwent reverse total shoulder arthroplasty (rTSA) for irreparable massive rotator cuff tears (mRCTs) under general anesthesia (GA) compared to those under interscalene block (IB). Methods: Forty elderly patients aged 65 years or older diagnosed with an irreparable mRCT who underwent rTSA were included in the prospective case-controlled study. Of these, 20 patients were operated under GA and the other 20 under IB. The average age was 77.1 years (range, 65–95 years). The severity of delirious symptoms was evaluated by the Delirium Rating Scale–revised–98 (DRS) score from the patients or guardians before the surgery and at 0, 3, and 7 days and 1, 3, and 6 months after the surgery and compared between the 2 groups. Results: Immediately after surgery, the visual analog scale score difference between the groups was statistically significant, with the GA group at 6.25 (standard deviation, ± 0.85) and the IB group at 3.80 (± 0.62) (p < 0.001). On the day of operation, the mean DRS score in the GA and IB groups were 9.10 (± 5.63) and 6.60 (± 5.33), respectively (p = 0.157). On day 3 of surgery, the mean DRS score in the GA group peaked to 9.95 (± 8.73), while in the IB group, it declined to 6.40 (±5.81) (p = 0.138). After 3 days, DRS scores showed a decreasing trend in both groups. When comparing the mean change (∆) from the preoperative baseline scores to the postoperative values, the ∆DRS score was significantly higher with 4.15 (± 4.53) points in the GA group as compared to 1.30 (± 1.92) in the IB group (p = 0.014). Conclusions IB can be an attractive and efficient anesthetic choice in preventing PD for elderly patients undergoing rTSA for irreparable mRCTs. The IB group showed lower DRS scores and a peak on day 0 compared to the higher DRS scores and peak on day 3 in the GA group. Additionally, IB showed less pain than GA.