Artificial Intelligence-Enabled POCUS in the COVID-19 ICU: A New Spin on Cardiac Ultrasound.
Ontology highlight
ABSTRACT:
SUBMITTER: Cheema BS
PROVIDER: S-EPMC7889018 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
Publications
Artificial Intelligence-Enabled POCUS in the COVID-19 ICU: A New Spin on Cardiac Ultrasound.
Cheema Baljash S BS Walter James J Narang Akhil A Thomas James D JD
JACC. Case reports 20210217 2
We present the novel use of a deep learning-derived technology trained on the skilled hand movements of cardiac sonographers that guides novice users to acquire high-quality bedside cardiac ultrasound images. We illustrate its use at the point of care through a series of patient encounters in the COVID-19 intensive care unit. (<b>Level of Difficulty: Beginner.</b>). ...[more]
Similar Datasets
Project description:Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85-0.91 for ECG and 0.89-1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3-4% at 52-71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74-77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.
| S-EPMC8113484 | biostudies-literature
Project description:Diagnostic support tools based on artificial intelligence (AI) have exhibited high performance in various medical fields. However, their clinical application remains challenging because of the lack of explanatory power in AI decisions (black box problem), making it difficult to build trust with medical professionals. Nevertheless, visualizing the internal representation of deep neural networks will increase explanatory power and improve the confidence of medical professionals in AI decisions. We propose a novel deep learning-based explainable representation "graph chart diagram" to support fetal cardiac ultrasound screening, which has low detection rates of congenital heart diseases due to the difficulty in mastering the technique. Screening performance improves using this representation from 0.966 to 0.975 for experts, 0.829 to 0.890 for fellows, and 0.616 to 0.748 for residents in the arithmetic mean of area under the curve of a receiver operating characteristic curve. This is the first demonstration wherein examiners used deep learning-based explainable representation to improve the performance of fetal cardiac ultrasound screening, highlighting the potential of explainable AI to augment examiner capabilities.
| S-EPMC8945208 | biostudies-literature
Project description:ObjectiveRespiratory symptoms are among the most common chief complaints of pediatric patients in the emergency department (ED). Point-of-care ultrasound (POCUS) outperforms conventional chest X-ray and is user-dependent, which can be challenging to novice ultrasound (US) users. We introduce a novel concept using artificial intelligence (AI)-enhanced pleural sweep to generate complete panoramic views of the lungs, and then assess its accuracy among novice learners (NLs) to identify pneumonia.MethodsPreviously healthy 0- to 17-year-old patients presenting to a pediatric ED with cardiopulmonary chief complaint were recruited. NLs received a 1-hour training on traditional lung POCUS and the AI-assisted software. Two POCUS-trained experts interpreted the images, which served as the criterion standard. Both expert and learner groups were blinded to each other's interpretation, patient data, and outcomes. Kappa was used to determine agreement between POCUS expert interpretations.ResultsSeven NLs, with limited to no prior POCUS experience, completed examinations on 32 patients. The average patient age was 5.53 years (±1.07). The median scan time of 7 minutes (minimum-maximum 3-43; interquartile 8). Three (8.8%) patients were diagnosed with pneumonia by criterion standard. Sensitivity, specificity, and accuracy for NLs AI-augmented interpretation were 66.7% (confidence interval [CI] 9.4-99.1%), 96.5% (CI 82.2-99.9%), and 93.7% (CI 79.1-99.2%). The average image quality rating was 2.94 (±0.16) out of 5 across all lung fields. Interrater reliability between expert sonographers was high with a kappa coefficient of 0.8.ConclusionThis study shows that AI-augmented lung US for diagnosing pneumonia has the potential to increase accuracy and efficiency.
| S-EPMC9790545 | biostudies-literature
Project description:Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.
| S-EPMC11108784 | biostudies-literature
Project description:BackgroundPoint-of-care ultrasound (POCUS) has been recognized as an essential skill across medicine. However, a lack of reliable and streamlined POCUS assessment tools with demonstrated validity remains a significant barrier to widespread clinical integration. The ultrasound competency assessment tool (UCAT) was derived to be a simple, entrustment-based competency assessment tool applicable to multiple POCUS applications. When used to assess a FAST, the UCAT demonstrated high internal consistency and moderate-to-excellent inter-rater reliability. The objective of this study was to validate the UCAT for assessment of a four-view transthoracic cardiac POCUS.ResultsTwenty-two trainees performed a four-view transthoracic cardiac POCUS in a simulated environment while being assessed by two observers. When used to assess a four-view cardiac POCUS the UCAT retained its high internal consistency ([Formula: see text] and moderate-to-excellent inter-rater reliability (ICCs = 0.61-0.91; p's ≤ 0.01) across all domains. The regression analysis suggestion that level of training, previous number of focused cardiac ultrasound, previous number of total scans, self-rated entrustment, and intent to pursue certification statistically significantly predicted UCAT entrustment scores [F (5,16) = 4.06, p = 0.01; R2 = 0.56].ConclusionThis study confirms the UCAT is a valid assessment tool for four-view transthoracic cardiac POCUS. The findings from this work and previous studies on the UCAT demonstrate the utility and flexibility of the UCAT tool across multiple POCUS applications and present a promising way forward for POCUS competency assessment.
| S-EPMC8476706 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) methods and AI-enabled metrics hold tremendous potential to advance surgical education. Our objective was to generate consensus guidance on specific needs for AI methods and AI-enabled metrics for surgical education.Study designThe study included a systematic literature search, a virtual conference, and a 3-round Delphi survey of 40 representative multidisciplinary stakeholders with domain expertise selected through purposeful sampling. The accelerated Delphi process was completed within 10 days. The survey covered overall utility, anticipated future (10-year time horizon), and applications for surgical training, assessment, and feedback. Consensus was agreement among 80% or more respondents. We coded survey questions into 11 themes and descriptively analyzed the responses.ResultsThe respondents included surgeons (40%), engineers (15%), affiliates of industry (27.5%), professional societies (7.5%), regulatory agencies (7.5%), and a lawyer (2.5%). The survey included 155 questions; consensus was achieved on 136 (87.7%). The panel listed 6 deliverables each for AI-enhanced learning curve analytics and surgical skill assessment. For feedback, the panel identified 10 priority deliverables spanning 2-year (n = 2), 5-year (n = 4), and 10-year (n = 4) timeframes. Within 2 years, the panel expects development of methods to recognize anatomy in images of the surgical field and to provide surgeons with performance feedback immediately after an operation. The panel also identified 5 essential that should be included in operative performance reports for surgeons.ConclusionsThe Delphi panel consensus provides a specific, bold, and forward-looking roadmap for AI methods and AI-enabled metrics for surgical education.
| S-EPMC10634198 | biostudies-literature
Project description:BackgroundThe incorporation of artificial intelligence (AI) into point-of-care ultrasound (POCUS) platforms has rapidly increased. The number of B-lines present on lung ultrasound (LUS) serve as a useful tool for the assessment of pulmonary congestion. Interpretation, however, requires experience and therefore AI automation has been pursued. This study aimed to test the agreement between the AI software embedded in a major vendor POCUS system and visual expert assessment.MethodsThis single-center prospective study included 55 patients hospitalized for various respiratory symptoms, predominantly acutely decompensated heart failure. A 12-zone protocol was used. Two experts in LUS independently categorized B-lines into 0, 1-2, 3-4, and ≥ 5. The intraclass correlation coefficient (ICC) was used to determine agreement.ResultsA total of 672 LUS zones were obtained, with 584 (87%) eligible for analysis. Compared with expert reviewers, the AI significantly overcounted number of B-lines per patient (23.5 vs. 2.8, p < 0.001). A greater proportion of zones with > 5 B-lines was found by the AI than by the reviewers (38% vs. 4%, p < 0.001). The ICC between the AI and reviewers was 0.28 for the total sum of B-lines and 0.37 for the zone-by-zone method. The interreviewer agreement was excellent, with ICCs of 0.92 and 0.91, respectively.ConclusionThis study demonstrated excellent interrater reliability of B-line counts from experts but poor agreement with the AI software embedded in a major vendor system, primarily due to overcounting. Our findings indicate that further development is needed to increase the accuracy of AI tools in LUS.
| S-EPMC11874383 | biostudies-literature
Project description:BackgroundPoint-of-care ultrasonography (POCUS) enables access to cardiac imaging directly at the bedside but is limited by brief acquisition, variation in acquisition quality, and lack of advanced protocols.ObjectiveTo develop and validate deep learning models for detecting underdiagnosed cardiomyopathies on cardiac POCUS, leveraging a novel acquisition quality-adapted modeling strategy.MethodsTo develop the models, we identified transthoracic echocardiograms (TTEs) of patients across five hospitals in a large U.S. health system with transthyretin amyloid cardiomyopathy (ATTR-CM, confirmed by Tc99m-pyrophosphate imaging), hypertrophic cardiomyopathy (HCM, confirmed by cardiac magnetic resonance), and controls enriched for the presence of severe AS. In a sample of 290,245 TTE videos, we used novel augmentation approaches and a customized loss function to weigh image and view quality to train a multi-label, view agnostic video-based convolutional neural network (CNN) to discriminate the presence of ATTR-CM, HCM, and/or AS. Models were tested across 3,758 real-world POCUS videos from 1,879 studies in 1,330 independent emergency department (ED) patients from 2011 through 2023.ResultsOur multi-label, view-agnostic classifier demonstrated state-of-the-art performance in discriminating ATTR-CM (AUROC 0.98 [95%CI: 0.96-0.99]) and HCM (AUROC 0.95 [95% CI: 0.94-0.96]) on standard TTE studies. Automated metrics of anatomical view correctness confirmed significantly lower quality in POCUS vs TTE videos (median view classifier confidence of 0.63 [IQR: 0.44-0.88] vs 0.93 [IQR: 0.69-1.00], p<0.001). When deployed to POCUS videos, our algorithm effectively discriminated ATTR-CM and HCM with AUROC of up to 0.94 (parasternal long-axis (PLAX)), and 0.85 (apical 4 chamber), corresponding to positive diagnostic odds ratios of 46.7 and 25.5, respectively. In total, 18/35 (51.4%) of ATTR-CM and 32/57 (41.1%) of HCM patients in the POCUS cohort had an AI-positive screen in the year before their eventual confirmatory imaging.ConclusionsWe define and validate an AI framework that enables scalable, opportunistic screening of under-diagnosed cardiomyopathies using POCUS.
| S-EPMC10980112 | biostudies-literature
Project description:BackgroundAutomated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent. We aimed to develop and validate an artificial intelligence-enabled ECG (AI-ECG) algorithm capable of comprehensive 12-lead ECG interpretation with accuracy comparable to practicing cardiologists.MethodsWe developed an AI-ECG algorithm using a convolutional neural network as a multilabel classifier capable of assessing 66 discrete, structured diagnostic ECG codes using the cardiologist's final annotation as the gold-standard interpretation. We included 2,499,522 ECGs from 720,978 patients ≥18 years of age with a standard 12-lead ECG obtained at the Mayo Clinic ECG laboratory between 1993 and 2017. The total sample was randomly divided into training (n = 1,749,654), validation (n = 249,951), and testing (n = 499,917) datasets with a similar distribution of codes. We compared the AI-ECG algorithm's performance to the cardiologist's interpretation in the testing dataset using receiver operating characteristic (ROC) and precision recall (PR) curves.ResultsThe model performed well for various rhythm, conduction, ischemia, waveform morphology, and secondary diagnoses codes with an area under the ROC curve of ≥0.98 for 62 of the 66 codes. PR metrics were used to assess model performance accounting for category imbalance and demonstrated a sensitivity ≥95% for all codes.ConclusionsAn AI-ECG algorithm demonstrates high diagnostic performance in comparison to reference cardiologist interpretation of a standard 12-lead ECG. The use of AI-ECG reading tools may permit scalability as ECG acquisition becomes more ubiquitous.
| S-EPMC8890098 | biostudies-literature
Project description:ImportanceLung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS.ObjectiveTo evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).Design, setting, and participantsIn this multicenter diagnostic validation study conducted between July 2023 and December 2023, participants aged 21 years or older with shortness of breath recruited from 4 clinical sites underwent 2 ultrasound examinations: 1 examination by a THCP operator using Lung Guidance AI and the other by a trained LUS expert without AI. The THCPs (including medical assistants, respiratory therapists, and nurses) underwent standardized AI training for LUS acquisition before participation.InterventionsLung Guidance AI software uses deep learning algorithms guiding LUS image acquisition and B-line annotation. Using an 8-zone LUS protocol, the AI software automatically captures images of diagnostic quality.Main outcomes and measuresThe primary end point was the proportion of THCP-acquired examinations of diagnostic quality according to a panel of 5 masked expert LUS readers, who provided remote review and ground truth validation.ResultsThe intention-to-treat analysis included 176 participants (81 female participants [46.0%]; mean [SD] age, 63 [14] years; mean [SD] body mass index, 31 [8]). Overall, 98.3% (95% CI, 95.1%-99.4%) of THCP-acquired studies were of diagnostic quality, with no statistically significant difference in quality compared to LUS expert-acquired studies (difference, 1.7%; 95% CI, -1.6% to 5.0%).Conclusions and relevanceIn this multicenter validation study, THCPs with AI assistance achieved LUS images meeting diagnostic standards compared with LUS experts without AI. This technology could extend access to LUS to underserved areas lacking expert personnel.Trial registrationClinicalTrials.gov Identifier: NCT05992324.
| S-EPMC11904735 | biostudies-literature