A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology.

This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023. For included studies, we extracted the NLP method (eg, rule-based, machine learning, deep learning, or hybrid), NLP application (eg, identification, classification, and automation), thyroid condition (eg, thyroid cancer, thyroid nodule, and functional or autoimmune disease), data source (eg, electronic health records, health forums, medical literature databases, or genomic databases), performance metrics, and stages of development. We identified 24 eligible NLP studies focusing on thyroid-related conditions. Deep learning-based methods were the most common (38%), followed by rule-based (21%), and traditional machine learning (21%) methods. Thyroid nodules (54%) and thyroid cancer (29%) were the primary conditions under investigation. Electronic health records were the dominant data source (17/24, 71%), with imaging reports being the most frequently used (15/17, 88%). There is increasing interest in NLP applications for thyroid-related studies, mostly addressing thyroid nodules and using deep learning-based methodologies with limited external validation. However, none of the reviewed NLP applications have reached clinical practice. Several limitations, including inconsistent clinical documentation and model portability, need to be addressed to promote the evaluation and implementation of NLP applications to support patient care in thyroidology.

Prognostic and Diagnostic Utility of Serum Biomarkers in Pediatric Traumatic Brain Injury.

Traumatic brain injury (TBI) remains a major cause of morbidity and death among the pediatric population. Timely diagnosis, however, remains a complex task because of the lack of standardized methods that permit its accurate identification. The aim of this study was to determine whether serum levels of brain injury biomarkers can be used as a diagnostic and prognostic tool in this pathology. This prospective, observational study collected and analyzed the serum concentration of neuronal injury biomarkers at enrollment, 24h and 48h post-injury, in 34 children ages 0-18 with pTBI and 19 healthy controls (HC). Biomarkers included glial fibrillary acidic protein (GFAP), neurofilament protein L (NfL), ubiquitin-C-terminal hydrolase (UCH-L1), S-100B, tau and tau phosphorylated at threonine 181 (p-tau181). Subjects were stratified by admission Glasgow Coma Scale score into two categories: a combined mild/moderate (GCS 9-15) and severe (GCS 3-8). Glasgow Outcome Scale-Extended (GOS-E) Peds was dichotomized into favorable (≤4) and unfavorable (≥5) and outcomes. Data were analyzed utilizing Prism 9 and R statistical software. The findings were as follows: 15 patients were stratified as severe TBI and 19 as mild/moderate per GCS. All biomarkers measured at enrollment were elevated compared with HC. Serum levels for all biomarkers were significantly higher in the severe TBI group compared with HC at 0, 24, and 48h. The GFAP, tau S100B, and p-tau181 had the ability to differentiate TBI severity in the mild/moderate group when measured at 0h post-injury. Tau serum levels were increased in the mild/moderate group at 24h. In addition, NfL and p-tau181 showed increased serum levels at 48h in the aforementioned GCS category. Individual biomarker performance on predicting unfavorable outcomes was measured at 0, 24, and 48h across different GOS-E Peds time points, which was significant for p-tau181 at 0h at all time points, UCH-L1 at 0h at 6-9 months and 12 months, GFAP at 48h at 12 months, NfL at 0h at 12 months, tau at 0h at 12 months and S100B at 0h at 12 months. We concluded that TBI leads to increased serum neuronal injury biomarkers during the first 0-48h post-injury. A biomarker panel measuring these proteins could aid in the early diagnosis of mild to moderate pTBI and may predict neurological outcomes across the injury spectrum.

Utility of Brain Injury Biomarkers in Children With Congenital Heart Disease Undergoing Cardiac Surgery.

BACKGROUND: Congenital heart disease (CHD) affects roughly 40,000 children annually. Despite advancements, children undergoing surgery for CHD are at an increased risk for adverse neurological outcomes. At present, there is no gold standard for the diagnosis of cerebral injury during the perioperative period. OBJECTIVE: To determine the utility of brain injury biomarkers in children undergoing cardiac surgery. METHODS: We searched PUBMED, EMBASE, LILACS, EBSCO, ClinicalTrials.gov, Cochrane Databases, and OVID interface to search MEDLINE through July 2021 and assessed the literature following the snowball method. The search terms used were "congenital heart disease," "cardiopulmonary bypass," "biomarkers," "diagnosis," "prognosis," and "children." No language or publication date restrictions were used. Papers studying inflammatory and imaging biomarkers were excluded. The risk of bias, strengths, and limitations of the study were reported. Study was registered in PROSPERO ID: CRD42021258385. RESULTS: A total of 1449 articles were retrieved, and 27 were included. Eight neurological biomarkers were examined. Outcomes assessed included prognosis of poor neurological outcome, mortality, readmission, and diagnosis of brain injury. Results from these studies support that significant perioperative elevations in brain injury biomarkers in cerebrospinal fluid and serum, including S100B, GFAP, NSE, and activin A, may be diagnostic of real-time brain injury and serve as an independent predictor of adverse neurological outcomes in patients with CHD undergoing cardiopulmonary bypass. CONCLUSIONS: There are limited homogeneous data in the field, limiting the generalizability and comparability of the results. Further large-scale longitudinal studies addressing neurological biomarkers in children undergoing CHD corrective surgery are required to support the routine use of neuronal biomarkers in this population.