Health system-scale language models are all-purpose prediction engines.

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1-3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7-94.9%, with an improvement of 5.36-14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care.

ChatGPT Solving Complex Kidney Transplant Cases: A Comparative Study With Human Respondents.

INTRODUCTION: ChatGPT has shown the ability to answer clinical questions in general medicine but may be constrained by the specialized nature of kidney transplantation. Thus, it is important to explore how ChatGPT can be used in kidney transplantation and how its knowledge compares to human respondents. METHODS: We prompted ChatGPT versions 3.5, 4, and 4 Visual (4 V) with 12 multiple-choice questions related to six kidney transplant cases from 2013 to 2015 American Society of Nephrology (ASN) fellowship program quizzes. We compared the performance of ChatGPT with US nephrology fellowship program directors, nephrology fellows, and the audience of the ASN's annual Kidney Week meeting. RESULTS: Overall, ChatGPT 4 V correctly answered 10 out of 12 questions, showing a performance level comparable to nephrology fellows (group majority correctly answered 9 of 12 questions) and training program directors (11 of 12). This surpassed ChatGPT 4 (7 of 12 correct) and 3.5 (5 of 12). All three ChatGPT versions failed to correctly answer questions where the consensus among human respondents was low. CONCLUSION: Each iterative version of ChatGPT performed better than the prior version, with version 4 V achieving performance on par with nephrology fellows and training program directors. While it shows promise in understanding and answering kidney transplantation questions, ChatGPT should be seen as a complementary tool to human expertise rather than a replacement.

Generating novel pituitary datasets from open-source imaging data and deep volumetric segmentation.

PURPOSE: The estimated incidence of pituitary adenomas in the general population is 10-30%, yet radiographic diagnosis remains a challenge. Diagnosis is complicated by the heterogeneity of radiographic features in both normal (e.g. complex anatomy, pregnancy) and pathologic states (e.g. primary endocrinopathy, hypophysitis). Clinical symptoms and laboratory testing are often equivocal, which can result in misdiagnosis or unnecessary specialist referrals. Computer vision models can aid in pituitary adenoma diagnosis; however, a major challenge to model development is the lack of dedicated pituitary imaging datasets. We hypothesized that deep volumetric segmentation models trained to extract the sellar and parasellar region from existing whole-brain MRI scans could be used to generate a novel dataset of pituitary imaging. METHODS: Six open-source whole-brain MRI datasets, created for research purposes, were included for model development. Deep learning-based volumetric segmentation models were trained using 318 manually annotated MRI scans from a single open-source MRI dataset. Out-of-distribution volumetric segmentation performance was then tested on 418 MRIs from five held-out research datasets. RESULTS: On our annotated images, agreement between manual and model volumetric segmentations was high. Dice scores (a measure of overlap) ranged 0.76-0.82 for both in-distribution and out-of-distribution model testing. In total, 6,755 MRIs from six data sources were included in the final generated pituitary dataset. CONCLUSIONS: We present the first and largest dataset of pituitary imaging constructed using existing MRI data and deep volumetric segmentation models trained to identify sellar and parasellar anatomy. The model generalizes well across patient populations and MRI scanner types. We hope our pituitary dataset will be an integral part of future machine learning research on pituitary pathologies.

Post-operative vision loss: analysis of 587 patients undergoing endoscopic surgery for pituitary macroadenoma.

PURPOSE: Vision loss following surgery for pituitary adenoma is poorly described in the literature and cannot be reliably predicted with current prognostic models. Detailed characterization of this population is warranted to further understand the factors that predispose a minority of patients to post-operative vision loss. MATERIALS AND METHODS: The medical records of 587 patients who underwent endoscopic transsphenoidal surgery at the Mount Sinai Medical Centre between January 2013 and August 2018 were reviewed. Patients who experienced post-operative vision deterioration, defined by reduced visual acuity, worsened VFDs, or new onset of blurry vision, were identified and analysed. RESULTS: Eleven out of 587 patients who received endoscopic surgery for pituitary adenoma exhibited post-operative vision deterioration. All eleven patients presented with preoperative visual impairment (average duration of 13.1 months) and pre-operative optic chiasm compression. Seven patients experienced visual deterioration within 24 h of surgery. The remaining four patients experienced delayed vision loss within one month of surgery. Six patients had complete blindness in at least one eye, one patient had complete bilateral blindness. Four patients had reduced visual acuity compared with preoperative testing, and four patients reported new-onset blurriness that was not present before surgery. High rates of graft placement (10/11 patients) and opening of the diaphragma sellae (9/11 patients) were found in this series. Four patients had hematomas and four patients had another significant post-operative complication. CONCLUSIONS: While most patients with pituitary adenoma experience favourable ophthalmological outcomes following endoscopic transsphenoidal surgery, a subset of patients exhibit post-operative vision deterioration. The present study reports surgical and disease features of this population to further our understanding of factors that may underlie vision loss following pituitary adenoma surgery. Graft placement and opening of the diaphragma sellae may be important risk factors in vision loss following ETS and should be an area of future investigation.

CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

MOTIVATION: Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. RESULTS: We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. AVAILABILITY AND IMPLEMENTATION: Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.

BACKGROUND: There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task. METHODS AND FINDINGS: A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong's test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855-0.866) on the joint MSH-NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both <0.001). The highest internal performance was achieved by combining training and test data from MSH and NIH (AUC 0.931, 95% CI 0.927-0.936), but this model demonstrated significantly lower external performance at IU (AUC 0.815, 95% CI 0.745-0.885, P = 0.001). To test the effect of pooling data from sites with disparate pneumonia prevalence, we used stratified subsampling to generate MSH-NIH cohorts that only differed in disease prevalence between training data sites. When both training data sites had the same pneumonia prevalence, the model performed consistently on external IU data (P = 0.88). When a 10-fold difference in pneumonia rate was introduced between sites, internal test performance improved compared to the balanced model (10× MSH risk P < 0.001; 10× NIH P = 0.002), but this outperformance failed to generalize to IU (MSH 10× P < 0.001; NIH 10× P = 0.027). CNNs were able to directly detect hospital system of a radiograph for 99.95% NIH (22,050/22,062) and 99.98% MSH (8,386/8,388) radiographs. The primary limitation of our approach and the available public data is that we cannot fully assess what other factors might be contributing to hospital system-specific biases. CONCLUSION: Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.

Natural Language-based Machine Learning Models for the Annotation of Clinical Radiology Reports.

Purpose To compare different methods for generating features from radiology reports and to develop a method to automatically identify findings in these reports. Materials and Methods In this study, 96 303 head computed tomography (CT) reports were obtained. The linguistic complexity of these reports was compared with that of alternative corpora. Head CT reports were preprocessed, and machine-analyzable features were constructed by using bag-of-words (BOW), word embedding, and Latent Dirichlet allocation-based approaches. Ultimately, 1004 head CT reports were manually labeled for findings of interest by physicians, and a subset of these were deemed critical findings. Lasso logistic regression was used to train models for physician-assigned labels on 602 of 1004 head CT reports (60%) using the constructed features, and the performance of these models was validated on a held-out 402 of 1004 reports (40%). Models were scored by area under the receiver operating characteristic curve (AUC), and aggregate AUC statistics were reported for (a) all labels, (b) critical labels, and (c) the presence of any critical finding in a report. Sensitivity, specificity, accuracy, and F1 score were reported for the best performing model's (a) predictions of all labels and (b) identification of reports containing critical findings. Results The best-performing model (BOW with unigrams, bigrams, and trigrams plus average word embeddings vector) had a held-out AUC of 0.966 for identifying the presence of any critical head CT finding and an average 0.957 AUC across all head CT findings. Sensitivity and specificity for identifying the presence of any critical finding were 92.59% (175 of 189) and 89.67% (191 of 213), respectively. Average sensitivity and specificity across all findings were 90.25% (1898 of 2103) and 91.72% (18 351 of 20 007), respectively. Simpler BOW methods achieved results competitive with those of more sophisticated approaches, with an average AUC for presence of any critical finding of 0.951 for unigram BOW versus 0.966 for the best-performing model. The Yule I of the head CT corpus was 34, markedly lower than that of the Reuters corpus (at 103) or I2B2 discharge summaries (at 271), indicating lower linguistic complexity. Conclusion Automated methods can be used to identify findings in radiology reports. The success of this approach benefits from the standardized language of these reports. With this method, a large labeled corpus can be generated for applications such as deep learning. © RSNA, 2018 Online supplemental material is available for this article.

Alterations of metabolic genes and metabolites in cancer.

Altered metabolic regulation has long been observed in human cancer and broadly used in the clinic for tumor detection. Two recent findings--the direct regulation of metabolic enzymes by frequently mutated cancer genes and frequent mutations of several metabolic enzymes themselves in cancer--have renewed interest in cancer metabolism. Supporting a causative role of altered metabolic enzymes in tumorigenesis, abnormal levels of several metabolites have been found to play a direct role in cancer development. The alteration of metabolic genes and metabolites offer not only new biomarkers for diagnosis and prognosis, but also potential new targets for cancer therapy.

Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial.

INTRODUCTION: Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH using deep learning algorithms without using advanced radiological features. METHODS: Data from the ATACH-2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage) was utilized. Variables included in the models were chosen as per literature consensus on salient variables associated with HE. HE was defined as increase in either >33% or 6 mL in hematoma volume in the first 24 h. Multiple machine learning algorithms were employed using iterative feature selection and outcome balancing methods. 70% of patients were used for training and 30% for internal validation. We compared the ML models to a logistic regression model and calculated AUC, accuracy, sensitivity and specificity for the internal validation models respective models. RESULTS: Among 1000 patients included in the ATACH-2 trial, 924 had the complete parameters which were included in the analytical cohort. The median [interquartile range (IQR)] initial hematoma volume was 9.93.mm3 [5.03-18.17] and 25.2% had HE. The best performing model across all feature selection groups and sampling cohorts was using an artificial neural network (ANN) for HE in the testing cohort with AUC 0.702 [95% CI, 0.631-0.774] with 8 hidden layer nodes The traditional logistic regression yielded AUC 0.658 [95% CI, 0.641-0.675]. All other models performed with less accuracy and lower AUC. Initial hematoma volume, time to initial CT head, and initial SBP emerged as most relevant variables across all best performing models. CONCLUSION: We developed multiple ML algorithms to predict HE with the ANN classifying the best without advanced radiographic features, although the AUC was only modestly better than other models. A larger, more heterogenous dataset is needed to further build and better generalize the models.

Transparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review.

Topic: This scoping review summarizes artificial intelligence (AI) reporting in ophthalmology literature in respect to model development and validation. We characterize the state of transparency in reporting of studies prospectively validating models for disease classification. Clinical Relevance: Understanding what elements authors currently describe regarding their AI models may aid in the future standardization of reporting. This review highlights the need for transparency to facilitate the critical appraisal of models prior to clinical implementation, to minimize bias and inappropriate use. Transparent reporting can improve effective and equitable use in clinical settings. Methods: Eligible articles (as of January 2022) from PubMed, Embase, Web of Science, and CINAHL were independently screened by 2 reviewers. All observational and clinical trial studies evaluating the performance of an AI model for disease classification of ophthalmic conditions were included. Studies were evaluated for reporting of parameters derived from reporting guidelines (CONSORT-AI, MI-CLAIM) and our previously published editorial on model cards. The reporting of these factors, which included basic model and dataset details (source, demographics), and prospective validation outcomes, were summarized. Results: Thirty-seven prospective validation studies were included in the scoping review. Eleven additional associated training and/or retrospective validation studies were included if this information could not be determined from the primary articles. These 37 studies validated 27 unique AI models; multiple studies evaluated the same algorithms (EyeArt, IDx-DR, and Medios AI). Details of model development were variably reported; 18 of 27 models described training dataset annotation and 10 of 27 studies reported training data distribution. Demographic information of training data was rarely reported; 7 of the 27 unique models reported age and gender and only 2 reported race and/or ethnicity. At the level of prospective clinical validation, age and gender of populations was more consistently reported (29 and 28 of 37 studies, respectively), but only 9 studies reported race and/or ethnicity data. Scope of use was difficult to discern for the majority of models. Fifteen studies did not state or imply primary users. Conclusion: Our scoping review demonstrates variable reporting of information related to both model development and validation. The intention of our study was not to assess the quality of the factors we examined, but to characterize what information is, and is not, regularly reported. Our results suggest the need for greater transparency in the reporting of information necessary to determine the appropriateness and fairness of these tools prior to clinical use. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Use of Carotid Web Angioarchitecture for Stroke Risk Assessment.

OBJECTIVE: To examine the usefulness of carotid web (CW), carotid bifurcation and their combined angioarchitectural measurements in assessing stroke risk. METHODS: Anatomic data on the internal carotid artery (ICA), common carotid artery (CCA), and the CW were gathered as part of a retrospective study from symptomatic (stroke) and asymptomatic (nonstroke) patients with CW. We built a model of stroke risk using principal-component analysis, Firth regression trained with 5-fold cross-validation, and heuristic binary cutoffs based on the Minimal Description Length principle. RESULTS: The study included 22 patients, with a mean age of 55.9 ± 12.8 years; 72.9% were female. Eleven patients experienced an ischemic stroke. The first 2 principal components distinguished between patients with stroke and patients without stroke. The model showed that ICA-pouch tip angle (P = 0.036), CCA-pouch tip angle (P = 0.036), ICA web-pouch angle (P = 0.036), and CCA web-pouch angle (P = 0.036) are the most important features associated with stroke risk. Conversely, CCA and ICA anatomy (diameter and angle) were not found to be risk factors. CONCLUSIONS: This pilot study shows that using data from computed tomography angiography, carotid bifurcation, and CW angioarchitecture may be used to assess stroke risk, allowing physicians to tailor care for each patient according to risk stratification.

Reliable Prediction of Discharge Disposition Following Cervical Spine Surgery With Ensemble Machine Learning and Validation on a National Cohort.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The purpose of this study is to develop a machine learning algorithm to predict nonhome discharge after cervical spine surgery that is validated and usable on a national scale to ensure generalizability and elucidate candidate drivers for prediction. SUMMARY OF BACKGROUND DATA: Excessive length of hospital stay can be attributed to delays in postoperative referrals to intermediate care rehabilitation centers or skilled nursing facilities. Accurate preoperative prediction of patients who may require access to these resources can facilitate a more efficient referral and discharge process, thereby reducing hospital and patient costs in addition to minimizing the risk of hospital-acquired complications. METHODS: Electronic medical records were retrospectively reviewed from a single-center data warehouse (SCDW) to identify patients undergoing cervical spine surgeries between 2008 and 2019 for machine learning algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for external validation of algorithm performance. Gradient-boosted trees were constructed to predict nonhome discharge across patient cohorts. The area under the receiver operating characteristic curve (AUROC) was used to measure model performance. SHAP values were used to identify nonlinear risk factors for nonhome discharge and to interpret algorithm predictions. RESULTS: A total of 3523 cases of cervical spine fusion surgeries were included from the SCDW data set, and 311,582 cases were isolated from NIS. The model demonstrated robust prediction of nonhome discharge across all cohorts, achieving an area under the receiver operating characteristic curve of 0.87 (SD=0.01) on both the SCDW and nationwide NIS test sets. Anterior approach only, age, elective admission status, Medicare insurance status, and total Elixhauser Comorbidity Index score were the most important predictors of discharge destination. CONCLUSIONS: Machine learning algorithms reliably predict nonhome discharge across single-center and national cohorts and identify preoperative features of importance following cervical spine fusion surgery.

Concepts and applications of digital twins in healthcare and medicine.

The digital twin (DT) is a concept widely used in industry to create digital replicas of physical objects or systems. The dynamic, bi-directional link between the physical entity and its digital counterpart enables a real-time update of the digital entity. It can predict perturbations related to the physical object's function. The obvious applications of DTs in healthcare and medicine are extremely attractive prospects that have the potential to revolutionize patient diagnosis and treatment. However, challenges including technical obstacles, biological heterogeneity, and ethical considerations make it difficult to achieve the desired goal. Advances in multi-modal deep learning methods, embodied AI agents, and the metaverse may mitigate some difficulties. Here, we discuss the basic concepts underlying DTs, the requirements for implementing DTs in medicine, and their current and potential healthcare uses. We also provide our perspective on five hallmarks for a healthcare DT system to advance research in this field.

Assessing superficial temporal artery-middle cerebral artery anastomosis patency using FLOW 800 hemodynamics.

OBJECTIVE: The objective of this study was to investigate the use of indocyanine green videoangiography with FLOW 800 hemodynamic parameters intraoperatively during superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery to predict patency prior to anastomosis performance. METHODS: A retrospective and exploratory data analysis was conducted using FLOW 800 software prior to anastomosis to assess four regions of interest (ROIs; proximal and distal recipients and adjacent and remote gyri) for four hemodynamic parameters (speed, delay, rise time, and time to peak). Medical records were used to classify patients into flow and no-flow groups based on immediate or perioperative anastomosis patency. Hemodynamic parameters were compared using univariate and multivariate analyses. Principal component analysis was used to identify high risk of no flow (HRnf) and low risk of no flow (LRnf) groups, correlated with prospective angiographic follow-ups. Machine learning models were fitted to predict patency using FLOW 800 features, and the a posteriori effect of complication risk of those features was computed. RESULTS: A total of 39 cases underwent STA-MCA bypass surgery with complete FLOW 800 data collection. Thirty-five cases demonstrated flow after anastomosis revascularization and were compared with 4 cases with no flow after revascularization. Proximal and distal recipient speeds were significantly different between the no-flow and flow groups (proximal: 238.3 ± 120.8 and 138.5 ± 93.6, respectively [p < 0.001]; distal: 241.0 ± 117.0 and 142.1 ± 103.8, respectively [p < 0.05]). Based on principal component analysis, the HRnf group (n = 10) was characterized by high-flow speed (> 75th percentile) in all ROIs, whereas the LRnf group (n = 10) had contrasting patterns. In prospective long-term follow-up, 6 of 9 cases in the HRnf group, including the original no-flow cases, had no or low flow, whereas 8 of 8 cases in the LRnf group maintained robust flow. Machine learning models predicted patency failure with a mean F1 score of 0.930 and consistently relied on proximal recipient speed as the most important feature. Computation of posterior likelihood showed a 95.29% chance of patients having long-term patency given a lower proximal speed. CONCLUSIONS: These results suggest that a high proximal speed measured in the recipient vessel prior to anastomosis can elevate the risk of perioperative no flow and long-term reduction of flow. With an increased dataset size, continued FLOW 800-based ROI metric analysis could be used to guide intraoperative anastomosis site selection prior to anastomosis and predict patency outcome.

Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark.

The detection and tracking of metastatic cancer over the lifetime of a patient remains a major challenge in clinical trials and real-world care. Advances in deep learning combined with massive datasets may enable the development of tools that can address this challenge. We present NYUMets-Brain, the world's largest, longitudinal, real-world dataset of cancer consisting of the imaging, clinical follow-up, and medical management of 1,429 patients. Using this dataset we developed Segmentation-Through-Time, a deep neural network which explicitly utilizes the longitudinal structure of the data and obtained state-of-the-art results at small (<10 mm3) metastases detection and segmentation. We also demonstrate that the monthly rate of change of brain metastases over time are strongly predictive of overall survival (HR 1.27, 95%CI 1.18-1.38). We are releasing the dataset, codebase, and model weights for other cancer researchers to build upon these results and to serve as a public benchmark.

Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fluorescence microscopy.

The most widely used fluorophore in glioma-resection surgery, 5-aminolevulinic acid (5-ALA), is thought to cause the selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumour cells. Here we show that the clinical detection of PpIX can be improved via a microscope that performs paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF). We validated the technique in fresh tumour specimens from 115 patients with high-grade gliomas across four medical institutions. We found a weak negative correlation between tissue cellularity and the fluorescence intensity of PpIX across all imaged specimens. Semi-supervised clustering of the TPEF images revealed five distinct patterns of PpIX fluorescence, and spatial transcriptomic analyses of the imaged tissue showed that myeloid cells predominate in areas where PpIX accumulates in the intracellular space. Further analysis of external spatially resolved metabolomics, transcriptomics and RNA-sequencing datasets from glioblastoma specimens confirmed that myeloid cells preferentially accumulate and metabolize PpIX. Our findings question 5-ALA-induced fluorescence in glioma cells and show how 5-ALA and TPEF imaging can provide a window into the immune microenvironment of gliomas.

Federated AI, Current State, and Future Potential.

Artificial intelligence and machine learning applications are becoming increasingly popular in health care and medical devices. The development of accurate machine learning algorithms requires large quantities of good and diverse data. This poses a challenge in health care because of the sensitive nature of sharing patient data. Decentralized algorithms through federated learning avoid data aggregation. In this paper we give an overview of federated learning, current examples in healthcare and ophthalmology, challenges, and next steps.

A Nationwide Study Characterizing the Risk and Outcome Profiles of Multilevel Fusion Procedures in Neuromuscular Scoliosis Patients with Neurofibromatosis Type 1.

BACKGROUND: Spine abnormalities are a common manifestation of Neurofibromatosis Type 1 (NF1); however, the outcomes of surgical treatment for NF1-associated spinal deformity are not well explored. The purpose of this study was to investigate the outcome and risk profiles of multilevel fusion surgery for NF1 patients. METHODS: The National Inpatient Sample was queried for NF1 and non-NF1 patient populations with neuromuscular scoliosis who underwent multilevel fusion surgery involving eight or more vertebral levels between 2004 and 2017. Multivariate regression modeling was used to explore the relationship between perioperative variables and pertinent outcomes. RESULTS: Of the 55,485 patients with scoliosis, 533 patients (0.96%) had NF1. Patients with NF1 were more likely to have comorbid solid tumors (P < 0.0001), clinical depression (P < 0.0001), peripheral vascular disease (P < 0.0001), and hypertension (P < 0.001). Following surgery, NF1 patients had a higher incidence of hydrocephalus (0.6% vs. 1.9% P = 0.002), seizures (4.9% vs. 5.7% P = 0.006), and accidental vessel laceration (0.3% vs.1.9% P = 0.011). Although there were no differences in overall complication rates or in-hospital mortality, multivariate regression revealed NF1 patients had an increased probability of pulmonary (OR 0.5, 95%CI 0.3-0.8, P = 0.004) complications. There were no significant differences in utilization, including nonhome discharge or extended hospitalization; however, patients with NF1 had higher total hospital charges (mean -$18739, SE 3384, P < 0.0001). CONCLUSIONS: These findings indicate that NF1 is associated with certain complications following multilevel fusion surgery but does not appear to be associated with differences in quality or cost outcomes. These results provide some guidance to surgeons and other healthcare professionals in their perioperative decision making by raising awareness about risk factors for NF1 patients undergoing multilevel fusion surgery. We intend for this study to set the national baseline for complications after multilevel fusion in the NF1 population.

A Century of Evolution in Spine Surgery Publications: A Bibliometric Analysis of the Field From 1900 to 2023.

BACKGROUND AND OBJECTIVES: Spine surgery has advanced in concert with our deeper understanding of its elements. Narrowly focused bibliometric analyses have been conducted previously, but never on the entire corpus of the field. Using big data and bibliometrics, we appraised the entire corpus of spine surgery publications to study the evolution of the specialty as a scholarly field since 1900. METHODS: We queried Web of Science for all contents from 13 major publications dedicated to spine surgery. We next queried by topic [topic = (spine OR spinal OR vertebrae OR vertebral OR intervertebral OR disc OR disk)]; these results were filtered to include articles published by 49 other publications that were manually determined to contain pertinent articles. Articles, along with their metadata, were exported. Statistical and bibliometric analyses were performed using the Bibliometrix R package and various Python packages. RESULTS: Eighty-five thousand five hundred articles from 62 journals and 134 707 unique authors were identified. The annual growth rate of publications was 2.78%, with a surge after 1980, concurrent with the growth of specialized journals. International coauthorship, absent before 1970, increased exponentially with the formation of influential spine study groups. Reference publication year spectroscopy allowed us to identify 200 articles that comprise the historical roots of modern spine surgery and each of its subdisciplines. We mapped the emergence of new topics and saw a recent lexical evolution toward outcomes- and patient-centric terms. Female and minority coauthorship has increased since 1990, but remains low, and disparities across major publications persist. CONCLUSION: The field of spine surgery was borne from pioneering individuals who published their findings in a variety of journals. The renaissance of spine surgery has been powered by international collaboration and is increasingly outcomes focused. While spine surgery is gradually becoming more diverse, there is a clear need for further promotion and outreach to under-represented populations.