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1.
J Community Health ; 2024 Aug 26.
Artigo em Inglês | MEDLINE | ID: mdl-39187724

RESUMO

The increasing reliance on digital tools for standard healthcare practices in uninsured populations is poorly understood. This study aims to assess the impacts of a newly implemented digital reimbursement system at a student-run primary care clinic associated with an academic medical institution serving uninsured New York City residents. Pharmacy records of 94 unique patients receiving a total of 2770 reimbursements between October 17th, 2016, and May 18th, 2023, were analyzed. Patients were divided into two groups (in-person vs. digital) based on their reimbursement preferences type. Demographic analyses were performed in addition to assessing reimbursement volumes, number of refunds, and duration until receipt of payment for each group. The clinic's total monthly reimbursement volume, number of prescriptions, and number of patients for the period before introduction of digital refunds was compared to the period after. The mean age (in-person = 52.7 ± 14.7 years, digital = 54.9 ± 12.9 years) was not statistically different between the groups. Patients in the digital group requested on average more refunds (digital = 47 refunds, in-person = 14 refunds), received higher total reimbursement amount (digital = $1131.24, in-person = $289.36), and they were reimbursed faster (digital = 56 days, in-person = 62 days). Since the introduction of the digital reimbursement option, our three-month reimbursement volume more than doubled from $481 to $1298. The average number of monthly reimbursements increased from 27 to 45 refunds, and the number of monthly patients increased from 6 to 9 patients. In summary, digital reimbursement options can facilitate medication reimbursement among uninsured patients. These results suggest that digital reimbursement systems result in higher utilization, faster refunds, and larger total reimbursements amount for uninsured and underserved patients.

2.
Eur Radiol ; 31(7): 4960-4971, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-33052463

RESUMO

OBJECTIVES: There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging. METHODS: Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set. RESULTS: Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p < 0.001) and specificity (0.92 vs 0.64, p < 0.001) with comparable sensitivity (0.75 vs 0.63, p = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p = 0.033) and specificity (0.92 vs 0.70, p < 0.001) with comparable sensitivity (0.75 vs 0.83, p = 0.557). Assisted by the model's probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p = 0.097) when compared with the senior radiologists. CONCLUSIONS: These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance. KEY POINTS: • Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists. • Assisted by the deep learning model's probabilities, junior radiologists achieved better performance that matched those of senior radiologists.


Assuntos
Aprendizado Profundo , Cistos Ovarianos , Neoplasias Ovarianas , Inteligência Artificial , Feminino , Humanos , Imageamento por Ressonância Magnética , Redes Neurais de Computação , Neoplasias Ovarianas/diagnóstico por imagem , Sensibilidade e Especificidade
5.
J Imaging Inform Med ; 2024 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-38886289

RESUMO

Two significant obstacles hinder the advancement of Radiology AI. The first is the challenge of overfitting, where small training data sets can result in unreliable outcomes. The second challenge is the need for more generalizability, the lack of which creates difficulties in implementing the technology across various institutions and practices. A recent innovation, deep neuroevolution (DNE), has been introduced to tackle the overfitting issue by training on small data sets and producing accurate predictions. However, the generalizability of DNE has yet to be proven. This paper strives to overcome this barrier by demonstrating that DNE can achieve satisfactory results in diverse external validation sets. The main innovation of the work is thus showing that DNE can generalize to varied outside data. Our example use case is predicting brain metastasis from neuroblastoma, emphasizing the importance of AI with limited data sets. Despite image collection and labeling advancements, rare diseases will always constrain data availability. We optimized a convolutional neural network (CNN) with DNE to demonstrate generalizability. We trained the CNN with 60 MRI images and tested it on a separate diverse collection of images from over 50 institutions. For comparison, we also trained with the more traditional stochastic gradient descent (SGD) method, with the two variants of (1) training from scratch and (2) transfer learning. Our results show that DNE demonstrates excellent generalizability with 97% accuracy on the heterogeneous testing set, while neither form of SGD could reach 60% accuracy. DNE's ability to generalize from small training sets to external and diverse testing sets suggests that it or similar approaches may play an integral role in improving the clinical performance of AI.

6.
Heliyon ; 9(6): e17309, 2023 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-37389076

RESUMO

Clinical practice guidelines and Scientific statements are influential publications that define the standard of care for many diseases. However, little is known about industry payments and financial conflict-of-interest among authors of such publications in cardiology. We identified guidelines published between 2014 and 2020 by the American Heart Association (AHA) and the American College of Cardiology (ACC) in order to assess the payment status of CPG authors using the Open Payment Program (OPP) database.

7.
JACC Clin Electrophysiol ; 9(4): 497-507, 2023 04.
Artigo em Inglês | MEDLINE | ID: mdl-36752460

RESUMO

BACKGROUND: Improved ablation catheter-tissue contact results in more effective ablation lesions. Respiratory motion causes catheter instability, which impacts durable pulmonary vein isolation (PVI). OBJECTIVES: This study sought to evaluate the safety and efficacy of a novel ablation strategy involving prolonged periods of apneic oxygenation during PVI. METHODS: We conducted a multicenter, prospective controlled study of 128 patients (mean age 63 ± 11 years; 37% women) with paroxysmal atrial fibrillation undergoing PVI. Patients underwent PVI under general anesthesia using serial 4-minute runs of apneic oxygenation (apnea group; n = 64) or using standard ventilation settings (control group; n = 64). Procedural data, arterial blood gas samples, catheter position coordinates, and ablation lesion characteristics were collected. RESULTS: Baseline characteristics between the 2 groups were similar. Catheter stability was significantly improved in the apnea group, as reflected by a decreased mean catheter displacement (1.55 ± 0.97 mm vs 2.25 ± 1.13 mm; P < 0.001) and contact force SD (4.9 ± 1.1 g vs 5.2 ± 1.5 g; P = 0.046). The percentage of lesions with a mean catheter displacement >2 mm was significantly lower in the apnea group (22% vs 44%; P < 0.001). Compared with the control group, the total ablation time to achieve PVI was reduced in the apnea group (18.8 ± 6.9 minutes vs 23.4 ± 7.8 minutes; P = 0.001). There were similar rates of first-pass PVI, acute PV reconnections and dormant PV reconnections between the two groups. CONCLUSIONS: A novel strategy of performing complete PVI during apneic oxygenation results in improved catheter stability and decreased ablation times without adverse events. (Radiofrequency Ablation of Atrial Fibrillation Under Apnea; NCT04170894).


Assuntos
Fibrilação Atrial , Ablação por Cateter , Veias Pulmonares , Humanos , Feminino , Pessoa de Meia-Idade , Idoso , Masculino , Veias Pulmonares/cirurgia , Estudos Prospectivos , Apneia/cirurgia , Apneia/etiologia , Ablação por Cateter/efeitos adversos , Ablação por Cateter/métodos
8.
Afr J Emerg Med ; 11(2): 299-302, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-33968606

RESUMO

INTRODUCTION: Traumatic injuries and their resulting mortality and disability impose a disproportionate burden on sub-Saharan countries like Rwanda. An important facet of addressing injury burdens is to comprehend injury patterns and aetiologies of trauma. This study is a cross-sectional analysis of injuries, treatments and outcomes at the University Teaching Hospital-Kigali (CHUK). METHODS: A random sample of Emergency Centre (EC) injury patients presenting during August 2015 through July 2016 was accrued. Patients were excluded if they had non-traumatic illness. Data included demographics, clinical presentation, injury type(s), mechanism of injury, and EC disposition. Descriptive statics were utilised to explore characteristics of the population. RESULTS: A random sample of 786 trauma patients met inclusion criteria and were analysed. The median age was 28 (IQR 6-50) years and 69.4% were male. Of all trauma patients 49.4% presented secondary to road traffic injuries (RTIs), 23.9% due to falls, 10.9% due to penetrating trauma. Craniofacial trauma was the most frequent traumatic injury location at 36.3%. Lower limb trauma and upper limb trauma constituted 35.8% and 27.1% of all injuries. Admission was required in 68.2% of cases, 23.3% were admitted to the orthopaedic service with the second highest admission to the surgical service (19.2%). Of those admitted to the hospital, the median LOS was 6 days (IQR 3-14), in the subset of patients requiring operative intervention, the median LOS was also 6 days (IQR 3-16). Death occurred in 5.5% of admitted patients in the hospital. CONCLUSION: The traumatic injury burden is borne more proportionally by young males in Kigali, Rwanda. Blunt trauma accounts for a majority of trauma patient presentations; of these RTIs constitute nearly half the injury mechanisms. These findings suggest that this population has substantial injury burdens and prevention and care interventions focused in this demographic group could provide positive impacts in the study setting.

9.
Abdom Radiol (NY) ; 46(6): 2656-2664, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-33386910

RESUMO

PURPOSE: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics. METHODS: A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT). RESULTS: The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. CONCLUSION: Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers.


Assuntos
Carcinoma de Células Renais , Neoplasias Renais , Carcinoma de Células Renais/diagnóstico por imagem , Carcinoma de Células Renais/cirurgia , Humanos , Neoplasias Renais/diagnóstico por imagem , Neoplasias Renais/cirurgia , Imageamento por Ressonância Magnética , Necrose , Estudos Retrospectivos
10.
Korean J Radiol ; 22(7): 1213-1224, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33739635

RESUMO

OBJECTIVE: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. MATERIALS AND METHODS: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. RESULTS: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. CONCLUSION: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.


Assuntos
COVID-19/diagnóstico , Aprendizado de Máquina , Índice de Gravidade de Doença , Tomografia Computadorizada por Raios X/métodos , Estado Terminal , Humanos , Masculino , Pessoa de Meia-Idade , Curva ROC , Estudos Retrospectivos , SARS-CoV-2/patogenicidade
11.
Sci Rep ; 10(1): 19503, 2020 11 11.
Artigo em Inglês | MEDLINE | ID: mdl-33177576

RESUMO

Pre-treatment determination of renal cell carcinoma aggressiveness may help guide clinical decision-making. We aimed to differentiate low-grade (Fuhrman I-II) from high-grade (Fuhrman III-IV) renal cell carcinoma using radiomics features extracted from routine MRI. 482 pathologically confirmed renal cell carcinoma lesions from 2008 to 2019 in a multicenter cohort were retrospectively identified. 439 lesions with information on Fuhrman grade from 4 institutions were divided into training and test sets with an 8:2 split for model development and internal validation. Another 43 lesions from a separate institution were set aside for independent external validation. The performance of TPOT (Tree-Based Pipeline Optimization Tool), an automatic machine learning pipeline optimizer, was compared to hand-optimized machine learning pipeline. The best-performing hand-optimized pipeline was a Bayesian classifier with Fischer Score feature selection, achieving an external validation ROC AUC of 0.59 (95% CI 0.49-0.68), accuracy of 0.77 (95% CI 0.68-0.84), sensitivity of 0.38 (95% CI 0.29-0.48), and specificity of 0.86 (95% CI 0.78-0.92). The best-performing TPOT pipeline achieved an external validation ROC AUC of 0.60 (95% CI 0.50-0.69), accuracy of 0.81 (95% CI 0.72-0.88), sensitivity of 0.12 (95% CI 0.14-0.30), and specificity of 0.97 (95% CI 0.87-0.97). Automated machine learning pipelines can perform equivalent to or better than hand-optimized pipeline on an external validation test non-invasively predicting Fuhrman grade of renal cell carcinoma using conventional MRI.


Assuntos
Carcinoma de Células Renais/diagnóstico , Neoplasias Renais/diagnóstico , Aprendizado de Máquina , Adulto , Idoso , Idoso de 80 Anos ou mais , Teorema de Bayes , Carcinoma de Células Renais/diagnóstico por imagem , Diagnóstico Diferencial , Feminino , Humanos , Neoplasias Renais/diagnóstico por imagem , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Gradação de Tumores/métodos , Curva ROC , Estudos Retrospectivos
12.
Clin Cancer Res ; 26(8): 1944-1952, 2020 04 15.
Artigo em Inglês | MEDLINE | ID: mdl-31937619

RESUMO

PURPOSE: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging. EXPERIMENTAL DESIGN: Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model. RESULTS: Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770). CONCLUSIONS: Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.


Assuntos
Algoritmos , Carcinoma de Células Renais/diagnóstico , Aprendizado Profundo , Neoplasias Renais/diagnóstico , Imageamento por Ressonância Magnética/métodos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Carcinoma de Células Renais/classificação , Criança , Pré-Escolar , Diagnóstico Diferencial , Feminino , Humanos , Neoplasias Renais/classificação , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Valor Preditivo dos Testes , Estudos Retrospectivos , Adulto Jovem
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