Citations

As artificial intelligence (AI) advances in healthcare, encompassing robust applications for the diagnosis and prognostication of musculoskeletal diseases, clinicians must increasingly understand the implications of machine learning and deep learning in their practice. This review article explores computer vision algorithms and patient-specific, multimodal prediction models; provides a simple framework to guide discussion on the limitations of AI model development; and introduces the field of generative AI.

Abstract Purpose: Germline alterations in homologous recombination repair (gHRR) genes affect the pathogenesis, treatment options, and survival of patients with cancer. However, distinct gHRR gene alterations may differentially affect treatment response and oncogenic signaling. In this study, we interrogated genomic and transcriptomic data and assessed clinical outcomes of patients with gHRR mutations across four BRCA-associated cancers (breast, ovarian, pancreatic, and prostate cancers) to identify therapeutic vulnerabilities. Experimental Design: We assessed 24,309 patients undergoing matched tumor/normal next-generation DNA and RNA sequencing. Annotated gHRR gene variants [germline BRCA1, germline BRCA2, germline PALB2, germline ATM (gATM), and germline CHEK2] were analyzed. HRs were used to assess survival outcomes comparing germline versus sporadic groups. Somatic alterations and their frequencies were compared across gHRR-altered groups. Differential gene expression and gene set enrichment analysis were used to compare transcriptomic profiles. Results: Somatic TP53 mutations were depleted in gATM carriers (P < 0.05) across all four BRCA-associated cancers by up to 2.5-fold. Tumors with germline BRCA1/2 mutations were associated with improved survival in patients with ovarian cancer and had consistent enrichment of TP53 mutations in all four cancers. gATM mutations displayed elevated p53 transcriptional activity in all four cancers, with significance reached in breast and prostate cancers (P < 0.01). In breast, ovarian, and prostate cancers, gATM tumors demonstrated significantly increased inflammatory pathways (P < 0.001). Finally, using gene dependency data, we found that cell lines that were highly dependent on ATM were co-dependent on canonical p53 function. Conclusions: gATM-associated cancers seem to require intact p53 activity and this synthetic essentiality may be used to guide targeted therapies that perturb canonical TP53 function.

Abstract Purpose Developing large‐scale, standardized radiographic registries for anterior cruciate ligament (ACL) injuries with artificial intelligence (AI) tools can enhance personalized orthopaedics. We propose deploying Artificial Intelligence for Knee Imaging Registration and Analysis (AKIRA), a trio of deep learning (DL) algorithms, to automatically classify and annotate radiographs. We hypothesize that algorithms can efficiently organize radiographs based on laterality, projection, identify implants and classify osteoarthritis (OA) grade. Methods A collection of 20,836 knee radiographs from all time points of treatment (mean orthopaedic follow‐up 70.7 months; interquartile range [IQR]: 6.8–172 months) were aggregated from 1628 ACL‐injured patients (median age 26 years [IQR: 19–42], 57% male). Three DL algorithms (EfficientNet, YOLO [You Only Look Once] and Residual Network) were employed. Radiograph laterality and projection (anterior‐posterior [AP], lateral, sunrise, posterior‐anterior, hip–knee–ankle and Camp‐Coventry intercondylar [notch]) were labelled by a DL model. Manually provided labels of metal fixation implants were used to develop a DL object detection algorithm. The degree of OA, both as measured by specific Kellgren–Lawrence (KL) grades, as well as based on a binarized label of OA (defined as KL Grade ≥2), on standing AP radiographs were classified using a DL algorithm. Individual model performances were evaluated on a subset of images prior to the deployment of AKIRA to registry construction using all ACL radiographs. Results The classification algorithms showed excellent performance in classifying radiographic laterality (F1 score: 0.962–0.975) and projection (F1 score: 0.941–1.0). The object detection algorithm achieved high precision–recall (area under the precision‐recall curve: 0.695–0.992) for identifying various metal fixations. The KL classifier reached concordances of 0.39–0.40, improving to 0.81–0.82 for binary OA labels. Sequential deployment of AKIRA following internal validation processed and labelled all 20,836 images with the appropriate views, implants, and the presence of OA within 88 min. Conclusion AKIRA effectively automated the classification and object detection in a large radiograph cohort of ACL injuries, creating an AI‐enabled radiographic registry with comprehensive details on laterality, projection, implants and OA. Study Design Cross‐sectional study. Level of Evidence Level IV.

Background/Objectives: The burden of scoliosis care extends beyond treatment costs and includes missed school for patients and lost income for parents. Chronic absenteeism, defined as more than 18 days of missed school, can have a significant impact on a child’s educational progression, but missed school and work due to scoliosis treatment are not well quantified in the literature. This study investigates absenteeism among scoliosis patients and their caregivers. Methods: We conducted a retrospective comparative study of survey results based on surgery timing and surgery type. Patients and caregivers presenting for clinic visits for scoliosis treatment at a single large tertiary care center from 2014 to 2022 were queried. Results: We collected 2772 surveys from 1104 unique patients. Of these, 223 surveys from 132 patients were within one year of surgery: 140 post-fusion surveys, 71 post-nonfusion surveys, and 11 post-halo/multistage surgery surveys. A total of 2280 surveys were from 1022 nonoperatively treated patients. School absenteeism was significantly higher for surgeries during the school year compared to summer in both the fusion and nonfusion groups, though work absenteeism showed no significant differences. Halo/multistage surgery patients had the highest rates of absenteeism. Conclusions: This study highlights the impacts of scoliosis surgery timing and type on absenteeism among patients and their caregivers. Surgery during summer breaks reduces school absenteeism and academic disruption. Halo/multistage surgery patients face the greatest risk of chronic absenteeism from school, indicating a need for targeted interventions. Optimized surgical timing and planning can help families navigate the educational and financial challenges of scoliosis treatment.

Summary: Although robotic surgery has been routinely established in other surgical disciplines, robotic technologies have been less readily adopted in plastic surgery. Despite a strong demand for innovation and cutting-edge technology in plastic surgery, most reconstructive procedures, including microsurgery, have continued to necessitate an open approach. Recent advances in robotics and artificial intelligence, however, are gaining momentum and have shown significant promise to improve patient care in plastic surgery. These next-generation surgical robots have the potential to enable surgeons to perform complex procedures with greater precision, flexibility, and control than previously possible with conventional techniques. Successful integration of robotic technologies into clinical practice in plastic surgery requires achieving key milestones, including implementing appropriate surgical education and garnering patient trust.

AbstractINTRODUCTIONAt the Alzheimer's Association's APOE and Immunity virtual conference, held in October 2021, leading neuroscience experts shared recent research advances on and inspiring insights into the various roles that both the apolipoprotein E gene (APOE) and facets of immunity play in neurodegenerative diseases, including Alzheimer's disease and other dementias.METHODSThe meeting brought together more than 1200 registered attendees from 62 different countries, representing the realms of academia and industry.RESULTSDuring the 4‐day meeting, presenters illuminated aspects of the cross‐talk between APOE and immunity, with a focus on the roles of microglia, triggering receptor expressed on myeloid cells 2 (TREM2), and components of inflammation (e.g., tumor necrosis factor α [TNFα]).DISCUSSIONThis manuscript emphasizes the importance of diversity in current and future research and presents an integrated view of innate immune functions in Alzheimer's disease as well as related promising directions in drug development.

Summary: Blockchain technology has attracted substantial interest in recent years, most notably for its effect on global economics through the advent of cryptocurrency. Within the health care domain, blockchain technology has been actively explored as a tool for improving personal health data management, medical device security, and clinical trial management. Despite a strong demand for innovation and cutting-edge technology in plastic surgery, integration of blockchain technologies within plastic surgery is in its infancy. Recent advances and mainstream adoption of blockchain are gaining momentum and have shown significant promise for improving patient care and information management. In this article, the authors explain what defines a blockchain and discuss its history and potential applications in plastic surgery. Existing evidence suggests that blockchain can enable patient-centered data management, improve privacy, and provide additional safeguards against human error. Integration of blockchain technology into clinical practice requires further research and development to demonstrate its safety and efficacy for patients and providers.

Background— Junctophilin-2 (JPH2), a protein expressed in the junctional membrane complex, is necessary for proper intracellular calcium (Ca 2+ ) signaling in cardiac myocytes. Downregulation of JPH2 expression in a model of cardiac hypertrophy was recently associated with defective coupling between plasmalemmal L-type Ca 2+ channels and sarcoplasmic reticular ryanodine receptors. However, it remains unclear whether JPH2 expression is altered in patients with hypertrophic cardiomyopathy (HCM). In addition, the effects of downregulation of JPH2 expression on intracellular Ca 2+ handling are presently poorly understood. We sought to determine whether loss of JPH2 expression is noted among patients with HCM and whether expression silencing might perturb Ca 2+ handling in a prohypertrophic manner. Methods and Results— JPH2 expression was reduced in flash-frozen human cardiac tissue procured from patients with HCM compared with ostensibly healthy traumatic death victims. Partial silencing of JPH2 expression in HL-1 cells by a small interfering RNA probe targeted to murine JPH2 mRNA (shJPH2) resulted in myocyte hypertrophy and increased expression of known markers of cardiac hypertrophy. Whereas expression levels of major Ca 2+ -handling proteins were unchanged, shJPH2 cells demonstrated depressed maximal Ca 2+ transient amplitudes that were insensitive to L-type Ca 2+ channel activation with JPH2 knockdown. Further, reduced caffeine-triggered sarcoplasmic reticulum store Ca 2+ levels were observed with potentially increased total Ca 2+ stores. Spontaneous Ca 2+ oscillations were elicited at a higher extracellular [Ca 2+ ] and with decreased frequency in JPH2 knockdown cells. Conclusions— Our results show that JPH2 levels are reduced in patients with HCM. Reduced JPH2 expression results in reduced excitation-contraction coupling gain as well as altered Ca 2+ homeostasis, which may be associated with prohypertrophic remodeling.