Clinical characteristics and outcome of omphalocele and gastroschisis: a 20-year multicenter regional experience.

Omphalocele and gastroschisis are the most common types of abdominal wall defects. Comprehensive local experience helps parents to make decisions on the pregnancy and foresee the disease journey. A retrospective review of abdominal wall defect patients in all three pediatric surgical centers in Hong Kong between January 2003 and February 2023 was conducted. All patients consecutively diagnosed with omphalocele and gastroschisis were included, excluding other forms. Data of demographics and short- and long-term outcome parameters were collected. A total of 99 cases were reviewed and 85 patients met the inclusion criteria. Diagnoses include omphalocele major (n = 49, 57.6%), omphalocele minor (n = 22, 25.9%) and gastroschisis (n = 14, 16.5%), with mean gestational age 37 weeks (SD 2.2) and birth weight 2.7 kg (SD 0.6). Omphalocele is most commonly associated with cardiovascular (n = 28, 39.4%) and chromosomal defects (n = 11, 15.5%). Surgical procedures including primary repair (n = 38, 53.5%), staged closure (n = 30, 42.3%) with average 8.6 days (SD 4.7) of silo reduction, and conservative management (n = 3, 4.2%) were performed. The mortality rate was 14.1% (n = 10) and the complication rate was 36.6% (n = 26). The majority of patients had normal intellectual development (92.5%) and growth (79.2%) on the latest follow-up. For gastroschisis, one patient (7.1%) had intestinal atresia. Surgical procedures included primary repair (n = 9, 64.3%) and staged closure (n = 5, 35.7%) with average 8 days (SD 3.5) of silo reduction. Complication rate was 21.4% (n = 3), with one mortality (7.1%). All patients had normal intellectual development and growth. The mean follow-up time of this series is 76.9 months (SD 62.9). Most abdominal wall defects in our series were managed surgically with a good overall survival rate and long-term outcome. This information is essential during antenatal and postnatal counseling for parents.

Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19.

OBJECTIVES: To develop and validate machine learning (ML) models to predict high-flow nasal cannula (HFNC) failure in COVID-19, compare their performance to the respiratory rate-oxygenation (ROX) index, and evaluate model accuracy by self-reported race. DESIGN: Retrospective cohort study. SETTING: Four Emory University Hospitals in Atlanta, GA. PATIENTS: Adult patients hospitalized with COVID-19 between March 2020 and April 2022 who received HFNC therapy within 24 hours of ICU admission were included. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Four types of supervised ML models were developed for predicting HFNC failure (defined as intubation or death within 7 d of HFNC initiation), using routine clinical variables from the first 24 hours of ICU admission. Models were trained on the first 60% (n = 594) of admissions and validated on the latter 40% (n = 390) of admissions to simulate prospective implementation. Among 984 patients included, 317 patients (32.2%) developed HFNC failure. eXtreme Gradient Boosting (XGB) model had the highest area under the receiver-operator characteristic curve (AUROC) for predicting HFNC failure (0.707), and was the only model with significantly better performance than the ROX index (AUROC 0.616). XGB model had significantly worse performance in Black patients compared with White patients (AUROC 0.663 vs. 0.808, p = 0.02). Racial differences in the XGB model were reduced and no longer statistically significant when restricted to patients with nonmissing arterial blood gas data, and when XGB model was developed to predict mortality (rather than the composite outcome of failure, which could be influenced by biased clinical decisions for intubation). CONCLUSIONS: Our XGB model had better discrimination for predicting HFNC failure in COVID-19 than the ROX index, but had racial differences in accuracy of predictions. Further studies are needed to understand and mitigate potential sources of biases in clinical ML models and to improve their equitability.

The limits of fair medical imaging AI in real-world generalization.

As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous research established AI's capacity to infer demographic data from chest X-rays, leading to a key concern: do models using demographic shortcuts have unfair predictions across subpopulations? In this study, we conducted a thorough investigation into the extent to which medical AI uses demographic encodings, focusing on potential fairness discrepancies within both in-distribution training sets and external test sets. Our analysis covers three key medical imaging disciplines-radiology, dermatology and ophthalmology-and incorporates data from six global chest X-ray datasets. We confirm that medical imaging AI leverages demographic shortcuts in disease classification. Although correcting shortcuts algorithmically effectively addresses fairness gaps to create 'locally optimal' models within the original data distribution, this optimality is not true in new test settings. Surprisingly, we found that models with less encoding of demographic attributes are often most 'globally optimal', exhibiting better fairness during model evaluation in new test environments. Our work establishes best practices for medical imaging models that maintain their performance and fairness in deployments beyond their initial training contexts, underscoring critical considerations for AI clinical deployments across populations and sites.

Synthetically enhanced: unveiling synthetic data's potential in medical imaging research.

BACKGROUND: Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, particularly denoising diffusion probabilistic models (DDPMs), offers a promising approach to generating synthetic images, enhancing dataset diversity. This study investigates the impact of synthetic data supplementation on the performance and generalizability of medical imaging research. METHODS: The study employed DDPMs to create synthetic CXRs conditioned on demographic and pathological characteristics from the CheXpert dataset. These synthetic images were used to supplement training datasets for pathology classifiers, with the aim of improving their performance. The evaluation involved three datasets (CheXpert, MIMIC-CXR, and Emory Chest X-ray) and various experiments, including supplementing real data with synthetic data, training with purely synthetic data, and mixing synthetic data with external datasets. Performance was assessed using the area under the receiver operating curve (AUROC). FINDINGS: Adding synthetic data to real datasets resulted in a notable increase in AUROC values (up to 0.02 in internal and external test sets with 1000% supplementation, p-value <0.01 in all instances). When classifiers were trained exclusively on synthetic data, they achieved performance levels comparable to those trained on real data with 200%-300% data supplementation. The combination of real and synthetic data from different sources demonstrated enhanced model generalizability, increasing model AUROC from 0.76 to 0.80 on the internal test set (p-value <0.01). INTERPRETATION: Synthetic data supplementation significantly improves the performance and generalizability of pathology classifiers in medical imaging. FUNDING: Dr. Gichoya is a 2022 Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program and declares support from RSNA Health Disparities grant (#EIHD2204), Lacuna Fund (#67), Gordon and Betty Moore Foundation, NIH (NIBIB) MIDRC grant under contracts 75N92020C00008 and 75N92020C00021, and NHLBI Award Number R01HL167811.

Complications of Nasal Tip Stabilizing Grafts with Autologous Versus Irradiated Homologous Costal Cartilage in Septorhinoplasty: A Systematic Review.

Background: Complications associated with the use of autologous and homologous costal cartilage for nasal tip stabilizing grafts in septorhinoplasty are not well understood. Objective: The authors review current literature to evaluate complications associated with autologous and irradiated homologous costal cartilage (IHCC) used for septal extension and columellar strut grafts in rhinoplasty. Method: A comprehensive literature search was conducted in PubMed, Embase, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov. Articles that used autologous or IHCC for either septal extension or columellar strut grafts in patients undergoing septorhinoplasty were included. The primary outcomes analyzed were postoperative complications. Results: A total of 14 studies representing 1358 patients were included. The pooled complication rate was 4.7%. IHCC grafts were associated with a higher incidence of complications (n = 21, 5.0% vs. n = 44, 4.6%, p = 0.01). Resorption was the most common complication in the IHCC group and occurred significantly more frequently than in the autologous costal cartilage (ACC) group (n = 10, 2.4% vs. n = 5, 0.49%, p = 0.002). Deviation/warping was the most common complication in the ACC group (n = 16, 1.7%). Conclusion: Autologous and irradiated homologous costal rhinoplasties remain safe procedures. The increased incidence of resorption associated with IHCC grafts should be considered during preoperative planning.

A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora of available tools for data curation in different stages, it is crucial to stay informed about the most relevant tools within specific research areas. In the current work, we propose a descriptive outline for different steps of data curation while we furnish compilations of tools collected from a survey applied among members of the Society of Imaging Informatics (SIIM) for each of these stages. This collection has the potential to enhance the decision-making process for researchers as they select the most appropriate tool for their specific tasks.

Utility of skin tone on pulse oximetry in critically ill patients: a prospective cohort study.

Importance: Pulse oximetry, a ubiquitous vital sign in modern medicine, has inequitable accuracy that disproportionately affects Black and Hispanic patients, with associated increases in mortality, organ dysfunction, and oxygen therapy. Although the root cause of these clinical performance discrepancies is believed to be skin tone, previous retrospective studies used self-reported race or ethnicity as a surrogate for skin tone. Objective: To determine the utility of objectively measured skin tone in explaining pulse oximetry discrepancies. Design Setting and Participants: Admitted hospital patients at Duke University Hospital were eligible for this prospective cohort study if they had pulse oximetry recorded up to 5 minutes prior to arterial blood gas (ABG) measurements. Skin tone was measured across sixteen body locations using administered visual scales (Fitzpatrick Skin Type, Monk Skin Tone, and Von Luschan), reflectance colorimetry (Delfin SkinColorCatch [L*, individual typology angle {ITA}, Melanin Index {MI}]), and reflectance spectrophotometry (Konica Minolta CM-700D [L*], Variable Spectro 1 [L*]). Main Outcomes and Measures: Mean directional bias, variability of bias, and accuracy root mean square (ARMS), comparing pulse oximetry and ABG measurements. Linear mixed-effects models were fitted to estimate mean directional bias while accounting for clinical confounders. Results: 128 patients (57 Black, 56 White) with 521 ABG-pulse oximetry pairs were recruited, none with hidden hypoxemia. Skin tone data was prospectively collected using 6 measurement methods, generating 8 measurements. The collected skin tone measurements were shown to yield differences among each other and overlap with self-reported racial groups, suggesting that skin tone could potentially provide information beyond self-reported race. Among the eight skin tone measurements in this study, and compared to self-reported race, the Monk Scale had the best relationship with differences in pulse oximetry bias (point estimate: -2.40%; 95% CI: -4.32%, -0.48%; p=0.01) when comparing patients with lighter and dark skin tones. Conclusions and relevance: We found clinical performance differences in pulse oximetry, especially in darker skin tones. Additional studies are needed to determine the relative contributions of skin tone measures and other potential factors on pulse oximetry discrepancies.

Reanimation of the Lower Lip with the Anterior Belly of Digastric Transfer: A Systematic Review.

Background: Dynamic reanimation of the lower lip is a challenging issue for patients, with depressor asymmetry commonly addressed with chemodenervation, selective neurectomy, or myectomy. Objective: To determine whether the anterior belly of digastric transfer is an effective method of lower-lip reanimation for patients with either isolated marginal mandibular branch weakness or inadequate depressor function after hemifacial reanimation, as measured by patient satisfaction and objective symmetry evaluation. Method: Systematic review of the literature was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Abstracts and full texts were reviewed. A Risk of Bias assessment was performed. Results: Nine studies with 164 patients were included. Anterior belly of digastric transfer was successfully performed in 162 patients. Most patients (52%) underwent one-staged reanimation innervated by the native nerve to the mylohyoid. A two-staged approach after placement of a cross face nerve graft was performed in 46%. Patient satisfaction was excellent (90.6%), with minimal complications including revision (4/162), infection (4/162), and lipofilling (8/162). Conclusion: In patients seeking a permanent outcome, use of an anterior belly of digastric transfer in either a one-stage or two-stage approach appears to be a safe and effective method to restore symmetry and dynamic function.

Efficient adversarial debiasing with concept activation vector - Medical image case-studies.

BACKGROUND: A major hurdle for the real time deployment of the AI models is ensuring trustworthiness of these models for the unseen population. More often than not, these complex models are black boxes in which promising results are generated. However, when scrutinized, these models begin to reveal implicit biases during the decision making, particularly for the minority subgroups. METHOD: We develop an efficient adversarial de-biasing approach with partial learning by incorporating the existing concept activation vectors (CAV) methodology, to reduce racial disparities while preserving the performance of the targeted task. CAV is originally a model interpretability technique which we adopted to identify convolution layers responsible for learning race and only fine-tune up to that layer instead of fine-tuning the complete network, limiting the drop in performance RESULTS:: The methodology has been evaluated on two independent medical image case-studies - chest X-ray and mammograms, and we also performed external validation on a different racial population. On the external datasets for the chest X-ray use-case, debiased models (averaged AUC 0.87 ) outperformed the baseline convolution models (averaged AUC 0.57 ) as well as the models trained with the popular fine-tuning strategy (averaged AUC 0.81). Moreover, the mammogram models is debiased using a single dataset (white, black and Asian) and improved the performance on an external datasets (averaged AUC 0.8 to 0.86 ) with completely different population (primarily Hispanic patients). CONCLUSION: In this study, we demonstrated that the adversarial models trained only with internal data performed equally or often outperformed the standard fine-tuning strategy with data from an external setting. The adversarial training approach described can be applied regardless of predictor's model architecture, as long as the convolution model is trained using a gradient-based method. We release the training code with academic open-source license - https://github.com/ramon349/JBI2023_TCAV_debiasing.

Identifying and improving the "ground truth" of race in disparities research through improved EMR data reporting. A systematic review.

BACKGROUND: Studies about racial disparities in healthcare are increasing in quantity; however, they are subject to vast differences in definition, classification, and utilization of race/ethnicity data. Improved standardization of this information can strengthen conclusions drawn from studies using such data. The objective of this study is to examine how data related to race/ethnicity are recorded in research through examining articles on race/ethnicity health disparities and examine problems and solutions in data reporting that may impact overall data quality. METHODS: In this systematic review, Business Source Complete, Embase.com, IEEE Xplore, PubMed, Scopus and Web of Science Core Collection were searched for relevant articles published from 2000 to 2020. Search terms related to the concepts of electronic medical records, race/ethnicity, and data entry related to race/ethnicity were used. Exclusion criteria included articles not in the English language and those describing pediatric populations. Data were extracted from published articles. This review was organized and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement for systematic reviews. FINDINGS: In this systematic review, 109 full text articles were reviewed. Weaknesses and possible solutions have been discussed in current literature, with the predominant problem and solution as follows: the electronic medical record (EMR) is vulnerable to inaccuracies and incompleteness in the methods that research staff collect this data; however, improved standardization of the collection and use of race data in patient care may help alleviate these inaccuracies. INTERPRETATION: Conclusions drawn from large datasets concerning peoples of certain race/ethnic groups should be made cautiously, and a careful review of the methodology of each publication should be considered prior to implementation in patient care.

Deformity, etiology, solution, sequence (DESS): Facial analysis in rhinoplasty.

PURPOSE: Rhinoplasty is amongst the most challenging surgeries to perfect and can take decades. This process begins during residency; however, residents often have limited exposure to rhinoplasty during their training and lack a standardized method for systematically analyzing and formulating a surgical plan. The DESS (Deformity, Etiology, Solution, Sequence) is a novel educational format for residents that serves to increase their pre-operative comfort with the surgical evaluation and intraoperative planning for a rhinoplasty. MATERIALS AND METHODS: A qualitative study performed at a tertiary academic institution with an otolaryngology residency program evaluating three consecutive residency classes comprised of four residents per class. A 9-item questionnaire was distributed to measure change in resident comfort after utilizing the DESS during their facial plastics rotation. Questionnaire responses highlighted resident comfort with facial nasal analysis, identifying deformities, suggesting surgical maneuvers, and synthesizing a comprehensive surgical plan. RESULTS: Ten of the twelve residents surveyed responded. Of those that responded, comfort in facial nasal analysis, identification of common nasal deformities, surgical planning, and development of an overall surgical plan were significantly improved after completion of the facial plastic rotation. These residents largely attributed their success to the systematic educational format, with an average score of 4.8/5.0 (SD 0.42). CONCLUSION: While rhinoplasty is a challenging artform to master, systematic approaches to analysis and operative planning are vital for teaching and guiding residents. Through this novel methodology, residents display significant improvement in their comfort with facial nasal analysis and overall surgical preparation.

Facial Reanimation After Intratemporal Facial Nerve Schwannoma Resection: A Systematic Review.

Objective: To systematically analyze the outcomes of reanimation techniques that have been described for patients undergoing non-fascicle sparing resection of intratemporal facial schwannomas. Methods: A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines of the PubMed, MEDLINE, and Cochrane Central Register of Controlled Trials databases. Results: Eight hundred forty studies were screened with 22 meeting inclusion criteria comprising 266 patients. Most facial nerve reanimations (81.2%) were performed using an interposition nerve graft. The remaining patients underwent hypoglossal-facial nerve transposition (13.9%), primary anastomosis (3.4%), and free muscle transfer (0.1%). Of the reported interposition grafts, the two most utilized were the great auricular (113/199) and sural (86/199) nerves. Interposition nerve grafts resulted in significantly better outcomes in facial nerve function postoperatively than hypoglossal-facial transposition (3.48 vs. 3.92; p < 0.01). There was no difference between interposition grafts. Conclusion: This study systematically reports that interposition nerve grafts, after resection of intratemporal facial schwannoma, result in superior outcomes than hypoglossal-facial nerve transposition in these patients.

Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study.

PURPOSE: Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world. Unfortunately, COPD is often difficult to diagnose early when interventions can alter the disease course, and it is underdiagnosed or only diagnosed too late for effective treatment. Currently, spirometry is the gold standard for diagnosing COPD but it can be challenging to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may have the potential as a screening tool to identify patients with COPD who should undergo further testing or intervention. In this study, we used three CXR datasets alongside their respective electronic health records (EHR) to develop and externally validate our models. METHOD: To leverage the performance of convolutional neural network models, we proposed two fusion schemes: (1) model-level fusion, using Bootstrap aggregating to aggregate predictions from two models, (2) data-level fusion, using CXR image data from different institutions or multi-modal data, CXR image data, and EHR data for model training. Fairness analysis was then performed to evaluate the models across different demographic groups. RESULTS: Our results demonstrate that DL models can detect COPD using CXRs with an area under the curve of over 0.75, which could facilitate patient screening for COPD, especially in low-resource regions where CXRs are more accessible than spirometry. CONCLUSIONS: By using a ubiquitous test, future research could build on this work to detect COPD in patients early who would not otherwise have been diagnosed or treated, altering the course of this highly morbid disease.

How Would You Manage This Patient With Recurrent Diverticulitis? : Grand Rounds Discussion From Beth Israel Deaconess Medical Center.

Acute diverticulitis, which refers to inflammation or infection, or both, of a colonic diverticulum, is a common medical condition that may occur repeatedly in some persons. It most often manifests with left-sided abdominal pain, which may be associated with low-grade fever and other gastrointestinal symptoms. Complications may include abscess, fistula formation, perforation, and bowel obstruction. The American College of Physicians recently published practice guidelines on the diagnosis and management of acute diverticulitis, the role of colonoscopy after resolution, and interventions to prevent recurrence of this condition. Among the recommendations were the use of abdominal computed tomography (CT) scanning in cases where there was diagnostic uncertainty, initial management of uncomplicated cases in the outpatient setting without antibiotics, referral for colonoscopy after an initial episode if not performed recently, and discussion of elective surgery to prevent recurrent disease in patients with complicated diverticulitis or frequent episodes of uncomplicated disease. Here, 2 gastroenterologists with expertise in acute diverticulitis debate CT scanning for diagnosis, antibiotics for treatment, colonoscopy to screen for underlying malignancy, and elective surgery to prevent recurrent disease.

Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports.

PURPOSE: To evaluate the complexity of diagnostic radiology reports across major imaging modalities and the ability of ChatGPT (Early March 2023 Version, OpenAI, California, USA) to simplify these reports to the 8th grade reading level of the average U.S. adult. METHODS: We randomly sampled 100 radiographs (XR), 100 ultrasound (US), 100 CT, and 100 MRI radiology reports from our institution's database dated between 2022 and 2023 (N = 400). These were processed by ChatGPT using the prompt "Explain this radiology report to a patient in layman's terms in second person: ". Mean report length, Flesch reading ease score (FRES), and Flesch-Kincaid reading level (FKRL) were calculated for each report and ChatGPT output. T-tests were used to determine significance. RESULTS: Mean report length was 164 ± 117 words, FRES was 38.0 ± 11.8, and FKRL was 10.4 ± 1.9. FKRL was significantly higher for CT and MRI than for US and XR. Only 60/400 (15%) had a FKRL <8.5. The mean simplified ChatGPT output length was 103 ± 36 words, FRES was 83.5 ± 5.6, and FKRL was 5.8 ± 1.1. This reflects a mean decrease of 61 words (p < 0.01), increase in FRES of 45.5 (p < 0.01), and decrease in FKRL of 4.6 (p < 0.01). All simplified outputs had FKRL <8.5. DISCUSSION: Our study demonstrates the effective use of ChatGPT when tasked with simplifying radiology reports to below the 8th grade reading level. We report significant improvements in FRES, FKRL, and word count, the last of which requires modality-specific context.

Secondary Mitral Regurgitation: Cardiac Remodeling, Diagnosis, and Management.

Secondary mitral regurgitation (MR) refers to MR resulting from left ventricular or left atrial remodeling. In ischemic or nonischemic cardiomyopathy, left ventricular dilation (regional or global) leads to papillary muscle displacement, tethering, and leaflet malcoaptation. In atrial functional MR, MR occurs in patients with left atrial dilation and altered mitral annular geometry due to atrial fibrillation. In addition to cardiac remodeling, leaflet remodeling is increasingly recognized. Mitral leaflet tissue actively adapts through leaflet growth to ensure adequate coaptation. Leaflets, however, can also undergo maladaptive thickening and fibrosis, leading to increased stiffness. The balance of cardiac and leaflet remodeling is a key determinant in the development of secondary MR. Clinical management starts with detection, severity grading, and identification of the underlying mechanism, which relies heavily on echocardiography. Treatment of secondary MR consists of guideline-directed medical therapy, surgical repair or replacement, and transcatheter edge-to-edge repair. Based on a better understanding of pathophysiology, novel percutaneous mitral repair and replacement devices have been developed and clinical trials are underway.