Establishing imaging registries for large patient cohorts is challenging because manual labeling is tedious and relying solely on DICOM (digital imaging and communications in medicine) metadata can ...result in errors. We endeavored to establish an automated hip and pelvic radiography registry of total hip arthroplasty (THA) patients by utilizing deep-learning pipelines. The aims of the study were (1) to utilize these automated pipelines to identify all pelvic and hip radiographs with appropriate annotation of laterality and presence or absence of implants, and (2) to automatically measure acetabular component inclination and version for THA images.
We retrospectively retrieved 846,988 hip and pelvic radiography DICOM files from 20,378 patients who underwent primary or revision THA performed at our institution from 2000 to 2020. Metadata for the files were screened followed by extraction of imaging data. Two deep-learning algorithms (an EfficientNetB3 classifier and a YOLOv5 object detector) were developed to automatically determine the radiographic appearance of all files. Additional deep-learning algorithms were utilized to automatically measure the acetabular angles on anteroposterior pelvic and lateral hip radiographs. Algorithm performance was compared with that of human annotators on a random test sample of 5,000 radiographs.
Deep-learning algorithms enabled appropriate exclusion of 209,332 DICOM files (24.7%) as misclassified non-hip/pelvic radiographs or having corrupted pixel data. The final registry was automatically curated and annotated in <8 hours and included 168,551 anteroposterior pelvic, 176,890 anteroposterior hip, 174,637 lateral hip, and 117,578 oblique hip radiographs. The algorithms achieved 99.9% accuracy, 99.6% precision, 99.5% recall, and a 99.6% F1 score in determining the radiograph appearance.
We developed a highly accurate series of deep-learning algorithms to rapidly curate and annotate THA patient radiographs. This efficient pipeline can be utilized by other institutions or registries to construct radiography databases for patient care, longitudinal surveillance, and large-scale research. The stepwise approach for establishing a radiography registry can further be utilized as a workflow guide for other anatomic areas.
Diagnostic Level IV . See Instructions for Authors for a complete description of levels of evidence.
Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a ...large majority of this data is concealed in electronic health records only accessible by manual extraction, which takes extensive time and resources. Natural language processing (NLP), a field within artificial intelligence, may offer a viable alternative to manual extraction. Using NLP, a researcher can analyze written and spoken data and extract data in an organized manner suitable for future research and clinical use. This article will first discuss common subtasks involved in an NLP pipeline, including data preparation, modeling, analysis, and external validation, followed by examples of NLP projects. Challenges and limitations of NLP will be discussed, closing with future directions of NLP projects, including large language models.
The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research ...and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on “good” data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models.
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Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) share many clinical, pathological, and genetic features, but a detailed understanding of their associated ...transcriptional alterations across vulnerable cortical cell types is lacking. Here, we report a high-resolution, comparative single-cell molecular atlas of the human primary motor and dorsolateral prefrontal cortices and their transcriptional alterations in sporadic and familial ALS and FTLD. By integrating transcriptional and genetic information, we identify known and previously unidentified vulnerable populations in cortical layer 5 and show that ALS- and FTLD-implicated motor and spindle neurons possess a virtually indistinguishable molecular identity. We implicate potential disease mechanisms affecting these cell types as well as non-neuronal drivers of pathogenesis. Finally, we show that neuron loss in cortical layer 5 tracks more closely with transcriptional identity rather than cellular morphology and extends beyond previously reported vulnerable cell types.
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•Multi-region atlas of 625,973 cells from 73 ALS, FTLD, and control individuals•Alterations in sporadic and C9orf72+ familial cases are highly convergent•Highly affected cell types are enriched for known genetic risk factors•Vulnerability fingerprint transcends brain regions and canonical cell types
Cell-type-specific transcriptional changes across the ALS-FTLD disease spectrum are studied through an integrated single-cell atlas of the human motor and prefrontal cortices, revealing molecular fingerprints and disease signatures of vulnerable cell types as well as non-cell-autonomous pathological mechanisms.
Image data has grown exponentially as systems have increased their ability to collect and store it. Unfortunately, there are limits to human resources both in time and knowledge to fully interpret ...and manage that data. Computer Vision (CV) has grown in popularity as a discipline for better understanding visual data. Computer Vision has become a powerful tool for imaging analytics in orthopedic surgery, allowing computers to evaluate large volumes of image data with greater nuance than previously possible. Nevertheless, even with the growing number of uses in medicine, literature on the fundamentals of CV and its implementation is mainly oriented toward computer scientists rather than clinicians, rendering CV unapproachable for most orthopedic surgeons as a tool for clinical practice and research. The purpose of this article is to summarize and review the fundamental concepts of CV application for the orthopedic surgeon and musculoskeletal researcher.
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Abstract In this study, we conducted the clinicopathological characterization of a non-pathogenic FAdV-D serotype 11 strain MX95, isolated from healthy chickens, and its entire genome was sequenced. ...Experiments in SPF chickens revealed that the strain is a non-pathogenic virus that did not cause death at challenge doses of 1×106 TCID50. Additionally, the infection in SPF chickens caused no apparent damage in most of the organs analyzed by necropsy and histopathology, but it did cause inclusion body hepatitis; nevertheless it did not generate severe infectious clinical symptoms. The virus was detected in several chicken organs, including the lymphoid organs, by real-time polymerase chain reaction (PCR) until 42 days. The genome of FAdV-11 MX95 has a size of 44,326 bp, and it encodes 36 open reading frames (ORFs). Comparative analysis of the genome indicated only 0.8% dissimilarity with a highly virulent serotype 11 that was previously reported.
•Most deep learning (DL) in imaging studies focused on spinal conditions' detection and diagnosis.•A total of 92% of DL in imaging studies developed a new model while 8% validated a pre-existing ...one.•DL in medical imaging showed promising performance in improving clinical spine care.•Implementation or demonstration of DL in real-world situations was rare.
Artificial intelligence is a revolutionary technology that promises to assist clinicians in improving patient care. In radiology, deep learning (DL) is widely used in clinical decision aids due to its ability to analyze complex patterns and images. It allows for rapid, enhanced data, and imaging analysis, from diagnosis to outcome prediction. The purpose of this study was to evaluate the current literature and clinical utilization of DL in spine imaging.
This study is a scoping review and utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2012 to 2021. A search in PubMed, Web of Science, Embased, and IEEE Xplore databases with syntax specific for DL and medical imaging in spine care applications was conducted to collect all original publications on the subject. Specific data was extracted from the available literature, including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest.
A total of 365 studies (total sample of 232,394 patients) were included and grouped into 4 general applications: diagnostic tools, clinical decision support tools, automated clinical/instrumentation assessment, and clinical outcome prediction. Notable disparities exist in the selected algorithms and the training across multiple disparate databases. The most frequently used algorithms were U-Net and ResNet. A DL model was developed and validated in 92% of included studies, while a pre-existing DL model was investigated in 8%. Of all developed models, only 15% of them have been externally validated.
Based on this scoping review, DL in spine imaging is used in a broad range of clinical applications, particularly for diagnosing spinal conditions. There is a wide variety of DL algorithms, database characteristics, and training methods. Future studies should focus on external validation of existing models before bringing them into clinical use.
Highly virulent fowl aviadenoviruses (genus: Aviadenovirus) represent a significant risk in poultry farming that may contribute to increased mortality rates and may adversely affect the growth ...performance of poultry flocks. In this study, we performed the clinicopathological characterization of a FAdV strain SHP95 isolated from a commercial farm and its whole genome sequencing. The study revealed that the isolated strain is a highly virulent serotype 4 FAdV that can cause 100% mortality in day-old specific pathogen free (SPF) chickens with a dose of 2.5 × 10 ⁵ TCID ₅₀. At a lower viral dose (1.5 × 10 ⁴ TCID ₅₀), the infection in day-old SPF chickens caused 40% mortality and lesions characteristic for Hepatitis-hydropericardium syndrome (HHS). The viral strain was detectable by real time PCR in chicken organs, including the lymphoid organs until day 28 after infection. The whole genome assembly of strain SHP95 revealed a size of 45,641 bp, which encodes for 42 viral open reading frame (ORF). The comparative analysis in the genome shows 98.1% similarity between strain SHP95 and other FAdV-4 genomes reported. The major differences in the genome sequence between pathogenic and non-pathogenic fowl Adenovirus were identified in the right arm of the genome.
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