[Application of the artificial intelligence-rapid whole-genome sequencing diagnostic system in the neonatal/pediatric intensive care unit].
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ABSTRACT:
SUBMITTER: Luo F
PROVIDER: S-EPMC8140348 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Genetic disorders are the leading cause of infant morbidity and mortality. Due to the large number of genetic diseases, molecular and phenotype heterogeneity and often severe course, these diseases remain undiagnosed. In infants with a suspected acute monogenic disease, rapid whole-exome sequencing (R-WES) can be successfully performed. R-WES (singletons) was performed in 18 unrelated infants with a severe and/or progressing disease with the suspicion of genetic origin hospitalized in an Intensive Care Unit (ICU). Blood samples were also collected from the parents. The results from the R-WES were available after 5-14 days. A conclusive genetic diagnosis was obtained in 13 children, corresponding to an overall diagnostic yield of 72.2%. For nine patients, R-WES was used as a first-tier test. Eight patients were diagnosed with inborn errors of metabolism, mainly mitochondrial diseases. In two patients, the disease was possibly caused by variants in genes which so far have not been associated with human disease (NARS1 and DCAF5). R-WES proved to be an effective diagnostic tool for critically ill infants in ICUs suspected of having a genetic disorder. It also should be considered as a first-tier test after precise clinical description. The quickly obtained diagnosis impacts patient's medical management, and families can receive genetic counseling.
| S-EPMC7408678 | biostudies-literature
Project description:The rising global incidence of cervical cancer is estimated to have affected more than 600,000 women, and nearly 350,000 women are predicted to have died from the disease in 2020 alone. Novel advances in cancer prevention, screening, diagnosis and treatment have all but reduced the burden of cervical cancer in developed nations. Unfortunately, cervical cancer is still the number one gynecological cancer globally. A limiting factor in managing cervical cancer globally is access to healthcare systems and trained medical personnel. Any methodology or procedure that may simplify or assist cervical cancer screening is desirable. Herein, we assess the use of artificial intelligence (AI)-assisted colposcopy in a tertiary hospital cervical diagnostic pathology unit. The study group consisted of 48 women (mean age 34) who were referred to the clinic for a routine colposcopy by their gynecologist. Cervical images were taken by an EVA-Visualcheck TM colposcope and run through an AI algorithm that gave real-time binary results of the cervical images as being either normal or abnormal. The primary endpoint of the study assessed the AI algorithm's ability to correctly identify histopathology results of CIN2+ as being abnormal. A secondary endpoint was a comparison between the AI algorithm and the clinical assessment results. Overall, we saw lower sensitivity of AI (66.7%; 12/18) compared with the clinical assessment (100%; 18/18), and histopathology results as the gold standard. The positive predictive value (PPV) was comparable between AI (42.9%; 12/28) and the clinical assessment (41.8%; 18/43). The specificity, however, was higher in the AI algorithm (46.7%; 14/30) compared to the clinical assessment (16.7%; 5/30). Comparing the congruence between the AI algorithm and histopathology results showed agreement 54.2% of the time and disagreement 45.8% of the time. A trained colposcopist was in agreement 47.9% and disagreement 52.1% of the time. Assessing these results, there is currently no added benefit of using the AI algorithm as a tool of speeding up diagnosis. However, given the steady improvements in the AI field, we believe that AI-assisted colposcopy may be of use in the future.
| S-EPMC8774766 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) holds the promise of supporting nurses' clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios.ObjectiveThis study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care.MethodsFollowing a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria.ResultsThe titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail.ConclusionsThe results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care-specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.
| S-EPMC8669587 | biostudies-literature
Project description:AimsAn artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE).Methods and resultsWe included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02-1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93-0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG.ConclusionThe AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.
| S-EPMC8245143 | biostudies-literature
Project description:Hematology analyzers capable of performing complete blood count (CBC) have lagged in their prevalence at the point-of-care. Sight OLO (Sight Diagnostics, Israel) is a novel hematological platform which provides a 19-parameter, five-part differential CBC, and is designed to address the limitations in current point-of-care hematology analyzers using recent advances in artificial intelligence (AI) and computer vision. Accuracy, repeatability, and flagging capabilities of OLO were compared with the Sysmex XN-Series System (Sysmex, Japan). Matrix studies compared performance using venous, capillary and direct-from-fingerprick blood samples. Regression analysis shows strong concordance between OLO and the Sysmex XN, demonstrating that OLO performs with high accuracy for all CBC parameters. High repeatability and reproducibility were demonstrated for most of the testing parameters. The analytical performance of the OLO hematology analyzer was validated in a multicenter clinical laboratory setting, demonstrating its accuracy and comparability to clinical laboratory-based hematology analyzers. Furthermore, the study demonstrated the validity of CBC analysis of samples collected directly from fingerpricks.
| S-EPMC9290600 | biostudies-literature
Project description:IntroductionResident training has changed significantly in recent years, resulting in reduced experiences and practice. Because pediatric residents have fewer required intensive care unit (ICU) rotations, we introduced a neonatal ICU (NICU) boot camp (2014-2015) that continues today to prepare residents immediately prior to beginning an NICU rotation.MethodsThe NICU boot camp consists of three 1-hour sessions: two interactive lectures with case-based application and one hands-on, integrative learning using simulation. The sessions are designed to cover basic information to assist in daily rounding and decision making while in the NICU. After their NICU rotation, residents complete a 12-item questionnaire. Program evaluation includes direct observation during sessions, faculty debriefing, and a postprogram resident survey.ResultsFifty-seven residents participated; questionnaire responses were available from 46 (80.70%). Combined percentages of very useful and extremely useful responses for the three sessions were 82.61%, 78.26%, and 82.60%, with 86.95% for the overall program; 80.40% agreed that repeating boot camp prior to each NICU rotation would be useful. Analysis of narrative responses revealed that participation in boot camp enhanced residents' readiness and confidence for patient care in the NICU and as stated for each educational objective.DiscussionProgram evaluation results support highly effective and sustainable implementation and achievement of educational objectives. Minor refinements continue for enhancing active learning and content materials and for increasing rigor of program evaluation. Results also suggest that our boot camp may benefit other pediatric programs and serve as a model for use in other resident specialty programs.
| S-EPMC6342430 | biostudies-literature
Project description:BackgroundEffective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)-based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers.MethodWe conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient's glucose-insulin system. A total of 126?000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis.ResultsFor the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (<70 mg/dl), 0.09%. In addition, the average coefficient of variation (CV) was 11.1%.ConclusionsThis in silico study of an AI-based closed loop glucose controller shows that it may be able to improve on the results achieved by currently existing ICU-based PID/MPC controllers. If these results are confirmed in clinical testing, this AI-based controller could be used to create an artificial pancreas system for use in the ICU setting.
| S-EPMC5094333 | biostudies-literature
Project description:BackgroundRespiratory support is crucial for newborns with underdeveloped lung. The clinical outcomes of patients depend on the clinician's ability to recognize the status underlying the presented symptoms and signs. With the increasing number of high-risk infants, artificial intelligence (AI) should be considered as a tool for personalized neonatal care. Continuous monitoring of vital signs is essential in cardiorespiratory care. In this study, we developed deep learning (DL) prediction models for rapid and accurate detection of mechanical ventilation requirements in neonates using electronic health records (EHR).MethodsWe utilized data from the neonatal intensive care unit in a single center, collected between March 3, 2012, and March 4, 2022, including 1,394 patient records used for model development, consisting of 505 and 889 patients with and without invasive mechanical ventilation (IMV) support, respectively. The proposed model architecture includes feature embedding using feature-wise fully connected (FC) layers, followed by three bidirectional long short-term memory (LSTM) layers.ResultsA mean gestational age (GA) was 36.61 ± 3.25 weeks, and the mean birth weight was 2,734.01 ± 784.98 g. The IMV group had lower GA, birth weight, and longer hospitalization duration than the non-IMV group (P < 0.05). Our proposed model, tested on a dataset from March 4, 2019, to March 4, 2022. The mean AUROC of our proposed model for IMV support prediction performance demonstrated 0.861 (95%CI, 0.853-0.869). It is superior to conventional approaches, such as newborn early warning score systems (NEWS), Random Forest, and eXtreme gradient boosting (XGBoost) with 0.611 (95%CI, 0.600-0.622), 0.837 (95%CI, 0.828-0.845), and 0.0.831 (95%CI, 0.821-0.845), respectively. The highest AUPRC value is shown in the proposed model at 0.327 (95%CI, 0.308-0.347). The proposed model performed more accurate predictions as gestational age decreased. Additionally, the model exhibited the lowest alarm rate while maintaining the same sensitivity level.ConclusionDeep learning approaches can help accurately standardize the prediction of invasive mechanical ventilation for neonatal patients and facilitate advanced neonatal care. The results of predictive, recall, and alarm performances of the proposed model outperformed the other models.
| S-EPMC10591351 | biostudies-literature
Project description:BackgroundLong-term morbidity after pediatric intensive care unit (PICU) admission is a growing concern. Both critical illness and accompanying PICU treatments may impact neurocognitive development as assessed by its gold standard measure; intelligence. This meta-analysis and meta-regression quantifies intelligence outcome after PICU admission and explores risk factors for poor intelligence outcome.MethodsPubMed, Embase, CINAHL and PsycINFO were searched for relevant studies, published from database inception until September 7, 2021. Using random-effects meta-analysis, we calculated the standardized mean difference in full-scale intelligence quotient (FSIQ) between PICU survivors and controls across all included studies and additionally distinguishing between PICU subgroups based on indications for admission. Relation between demographic and clinical risk factors and study's FSIQ effect sizes was investigated using random-effects meta-regression analysis.ResultsA total of 123 articles was included, published between 1973 and 2021, including 8,119 PICU survivors and 1,757 controls. We found 0.47 SD (7.1 IQ-points) lower FSIQ scores in PICU survivors compared to controls (95%CI -0.55 to -0.40, p < .001). All studied PICU subgroups had lower FSIQ compared to controls (range 0.38-0.88 SD). Later year of PICU admission (range 1972-2016) and longer PICU stay were related to greater FSIQ impairment (R2 = 21%, 95%CI -0.021 to -0.007, p < .001 and R2 = 2%, 95%CI -0.027 to -0.002, p = .03, respectively), whereas male sex and higher rate of survivors were related to smaller FSIQ impairment (R2 = 5%, 95%CI 0.001 to 0.014, p = .03 and R2 = 11%, 95%CI 0.006 to 0.022, p < .001, respectively). Meta-regression in PICU subgroups showed that later year of PICU admission was related to greater FSIQ impairment in children admitted after cardiac surgery and heart- or heart-lung transplantation. Male sex was related to smaller FSIQ impairment in children admitted after cardiac surgery. Older age at PICU admission and older age at follow-up were related to smaller FSIQ impairment in children admitted after heart- or heart-lung transplantation.ConclusionsPICU survivors, distinguished in a wide range of subgroups, are at risk of intelligence impairment. Length of PICU stay, female sex and lower rate of survivors were related to greater intelligence impairment. Intelligence outcome has worsened over the years, potentially reflecting the increasing percentage of children surviving PICU admission.
| S-EPMC9158152 | biostudies-literature
Project description:AIM:To investigate the frequency, etiology, and current management strategies for diarrhea in newborn. METHODS:Retrospective, nationwide study involving 5801 subjects observed in neonatal intensive care units during 3 years. The main anamnesis and demographic characteristics, etiology and characteristics of diarrhea, nutritional and therapeutic management, clinical outcomes were evaluated. RESULTS:Thirty-nine cases of diarrhea (36 acute, 3 chronic) were identified. The occurrence rate of diarrhea was 6.72 per 1000 hospitalized newborn. Etiology was defined in 29 of 39 newborn (74.3%): food allergy (20.5%), gastrointestinal infections (17.9%), antibiotic-associated diarrhea (12.8%), congenital defects of ion transport (5.1%), withdrawal syndrome (5.1%), Hirschsprung's disease (2.5%), parenteral diarrhea (2.5%), cystic fibrosis (2.5%), and metabolic disorders (2.5%). Three patients died due to complications related to diarrhea (7.7%). In 19 of 39 patients (48.7%), rehydration was performed exclusively by the enteral route. CONCLUSION:Diarrhea in neonates is a challenging clinical condition due to the possible heterogeneous etiologies and severe outcomes. Specific guidelines are advocated in order to optimize management of diarrhea in this particular setting.
| S-EPMC2880780 | biostudies-literature