Disease Classification Research Articles

Insomnia is highly prevalent among military personnel, with many gaps in knowledge. The purpose of this study was to quantify the medical, psychiatric, and utilization burden of insomnia among active-duty military personnel. We hypothesized that insomnia is associated with worsened health and economic outcomes. This was a retrospective case-control study. Data were derived from the Military Data Repository (2016-2021). Active-duty service members (ADSMs) younger than 65 years, with 12 months of continuous enrollment before and after first insomnia diagnosis and no evidence of previous insomnia or insomnia treatment, were matched 1:1 on demographic, clinical, and military characteristics to ADSMs without insomnia. Insomnia and psychiatric and medical comorbidities were defined using International Classification of Diseases, 10th Revision diagnostic codes. The impact of newly diagnosed insomnia on psychiatric and medical outcomes within 12 months was examined using time-to-event models. The impact of newly diagnosed insomnia on 12-month health care resource utilization (HCRU) was examined using generalized linear models. A total of 40,978 ADSMs met insomnia criteria and were matched to 40,978 ADSMs without insomnia. Participants were 78.6% male and 61.8% identified as White, with most younger than 44 years (90.3%). Insomnia was associated with increased risk of almost every studied physical and psychological health outcomes; relative to those without insomnia, ADSMs with insomnia demonstrated a 6-fold increased risk of post-traumatic stress disorder (hazard ratio [HR] 6.51, 95% CI 5.95-7.12, p < 0.001), as well as elevated risk of traumatic brain injury (HR 5.32, 95% CI 4.53-6.24, p < 0.001). ADSMs with insomnia demonstrated greater all-cause HCRU across all points of service (all p's < 0.001). Among active-duty personnel, new-onset insomnia was associated with substantially increased risk of adverse medical and psychiatric burden, as well as increased utilization, over 12 months. Key limitations include our observational study design.

Although obesity is recognized as a risk factor of migraine chronification, its longitudinal impact on migraine onset remains unclear. The aim of this study was to investigate the association between obesity and the risk of developing migraine in a longitudinal setting. We conducted a population-based prospective cohort study using data from the Korean National Health Insurance Service. Individuals aged 20-39 years who underwent health examinations between 2009 and 2012 were enrolled, excluding those with a history of migraine, missing data, or a migraine diagnosis within the first year. Participants were followed to the end of 2018. Body mass index (BMI) was categorized into 5 groups and waist circumference (WC) into 6 levels. Outcome was the first migraine claim (International Classification of Diseases, Tenth Revision code G43). We evaluated the associations of BMI and WC with the risk of migraine using multivariable Cox proportional hazard regression models. Interaction analyses were conducted based on demographic and lifestyle factors. Among 6,106,560 individuals included (mean age: 30.6 ± 4.98 years; 39% female), the risk of migraine increased with increasing BMI (adjusted hazard ratio [aHR] 1.001 [95% CI 0.991-1.010], 1.047 [95% CI 1.040-1.055], 1.087 [95% CI 1.079-1.094], and 1.121 [95% CI 1.106-1.136] for underweight, overweight, stage 1 obesity, and stage 2 obesity, respectively, compared with normal BMI) and WC (aHR 0.913 [95% CI 0.906-0.921], 0.969 [95% CI 0.960-0.978], 1.022 [95% CI 1.010-1.034], 1.048 [95% CI 1.032-1.065], and 1.048 [95% CI 1.030-1.067] for levels 1, 2, 4, 5, and 6, respectively, compared with level 3). This dose-dependent relationship remained significant for WC after adjusting for BMI, but not vice versa. A stronger association between abdominal obesity and migraine risk was observed in younger individuals (<30 years) (p for interaction = 0.0181), nonsmokers (p for interaction = 0.0341), and heavy drinkers (p for interaction = 0.0143). Obesity was associated with an increased risk of migraine in young adults in a dose-dependent manner, with WC demonstrating a more robust independent association than BMI. Owing to the limitations of claims data, our study needs cautious interpretation and further validation in studies with detailed clinical information.

Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions.

Hierarchical graph-guided contextual representation learning for Neurodegenerative pattern recognition in MRI.

The early and accurate classification of eye diseases is essential for preventing irreversible visual impairment. This task can be performed by deep learning approaches that automatically classify retinal fundus images according to potential illnesses. Despite notable advances in this field, the robust and methodologically rigorous classification of a broad range of eye diseases remains unsolved. This study addresses this issue by proposing a novel deep learning architecture that leverages specific features of retinal fundus images (e.g., image noise and importance of fine structures) using a tailored software lens to robustly diagnose a broad spectrum of illnesses at a high performance level. To validate this approach, the currently broadest peer-reviewed dataset of 16,242 images, comprising nine diseases and healthy samples, is chosen. Our novel architecture achieves a 5-fold cross-validated average balanced accuracy of 82.52 %, outperforming the baseline model (79.40 %) and setting a new benchmark. Our results demonstrate for the first time that high performance can be achieved for diagnosing a broad range of eye diseases based on retinal fundus images by leveraging their specific features. This approach has implications for clinical deployment, particularly in routine care settings, by enabling faster and more reliable screenings.

COVID-19 is a extremely contagious disease triggered by the SARS-CoV-2 virus which mostly affects the human breathing system. Furthermore, the COVID-19 was emerged in late 2019 and escalated rapidly into a global pandemic which impacted health and economic challenges across globe. Similar to other infectious diseases, it transmits through respiratory droplets and the rapid diagnosis is more important in controlling transmission and managing patient health care. In this work, a deep learning framework for COVID-19 classification using cough sounds has been proposed. Furthermore, the various deep learning models such as one-dimensional Convolutional Neural Network (1D-CNN), Depth-wise Convolutional Neural Network (DS-CNN), EfficientNet v2 and ResNet are utilized for the identification of normal and cough sounds produced by COVID-19. Also, the performances of all the deep learning models are analyzed using performance metrics such as accuracy, recall, precision, Matthews Correlation Coefficient (MCC), F1_Score and False Positive Rate (FPR). Results demonstrate that the performance of pre-trained models namely EfficientNet v2 and ResNet is better when compared to existing Deep Learning (DL) models. Additionally, the accuracy, precision, recall, F1_Score, MCC and false positive rate of ResNet is 98.5%, 98.99, 98, 0.9849, 0.9699 and 0.01 respectively shows that the ResNet is superior to the other models. The proposed work focus on the early intervention which helps physicians to isolate or treat patients which reduces transmission.

Medical assessment of patients treated by dental school students with regard to medical history, medication use and allergies to determine potential medical risks of the changing population structure and to develop implications for future curriculum design. A cross-sectional study was conducted to assess the medical records of patients, treated between November 2020 and October 2021, for demographic data (age, sex), allergies, systemic disorders, existing diseases, and medication use. Diseases were categorized according to the International Classification of Diseases (ICD), while medication was classified based on the Anatomical Therapeutic Chemical (ATC) Classification. A statistical analysis of the correlations between patient characteristics and prevalence data was performed. Data of 297 participants were analysed, including 142 women (mean age 55.51 ± 14.9 yrs) and 155 men (54.91 ± 15.52 yrs). Systemic diseases were present in 189 individuals (63.6%), 178 (60.1%) were taking medication, and 138 (46.5%) had at least one allergy. Polypharmacy (≥ 3 medications) was observed in 28% of participants (mean age 62.4 years). Medication use and disease burden increased significantly with age (e.g., cardiovascular medication: OR = 1.09 per year; 95% CI: 1.07-1.12; p < 0.001). A statistically significant sex difference was observed for hormonal medication (ATC H: 68% female vs. 32% male; p = 0.0012). The observed advanced age profile of the patients and its correlation with the prevalence of systemic diseases, medication use, and allergies demonstrated the medical complexity of dental patients care. These observations emphasize the importance of providing undergraduates but also postgraduates with a more comprehensive medical education to prepare them to effectively treat medically complex patients.

To compare obstetric and perinatal outcomes between assisted reproductive technologies (ART)-conceived and spontaneously conceived twin pregnancies using a nationwide Korean cohort. This retrospective cohort study used Korean National Health Insurance Service data from October 2017 to December 2021. Twin pregnancies were identified via International Statistical Classification of Diseases and Related Health Problems, 10th revision codes and classified by conception type based on embryo transfer procedure codes. Outcomes included miscarriage, preeclampsia, placenta previa, gestational diabetes mellitus (GDM), emergency cesarean section (CS), intrauterine growth restriction (IUGR), and macrosomia. Multivariable logistic regression was used to calculate adjusted odds ratios (aOR), controlling for maternal age and comorbidities. Subgroup analyses stratified by maternal age were also performed. Among 36,013 twin pregnancies, those conceived via ART exhibited significantly higher risks of obstetric complications, including placenta previa (aOR, 1.81; 95% confidence interval [CI], 1.57-2.08), preeclampsia (aOR, 1.31; 95% CI, 1.17-1.47), GDM (aOR, 1.32; 95% CI, 1.22-1.43), emergency CS (aOR, 1.21; 95% CI, 1.08-1.34), and IUGR (aOR, 1.18; 95% CI, 1.07-1.31). In age-stratified analyses, the risks were more pronounced in women aged ≥35 years (preeclampsia: aOR, 1.38; 95% CI, 1.19-1.61; emergency CS: aOR, 1.22; 95% CI, 1.06-1.42) compared with those aged <35 years (preeclampsia: aOR, 1.26; 95% CI, 1.05-1.50; emergency CS: aOR, 1.17; 95% CI, 1.00-1.37). Twin pregnancies conceived via ART are associated with significantly increased risks of obstetric and perinatal complications compared with spontaneous conceptions. Given the growing utilization of ART, these findings underscore the importance of individualized prenatal care and vigilant perinatal monitoring in ART-conceived twin pregnancies, particularly among women of advanced maternal age.

Capsicum (bell pepper) is a globally important crop whose productivity is severely limited by leaf diseases, including bacterial spot, anthracnose, mosaic virus and powdery mildew. Early and accurate detection of these diseases through non-destructive imaging and machine intelligence is critical for yield protection. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have revolutionized plant disease identification, enabling automated pipelines that analyse leaf images and classify disease. This survey focuses specifically on the detection of Capsicum leaf disease using deep learning. We review major Capsicum-related image datasets (e.g. PlantVillage pepper images, the COLD chili-onion dataset, the BellCrop dataset and other recent curated collections) and summarize state-of-the-art deep models applied to Capsicum disease classification. Original figures illustrate a typical detection pipeline and a CNN architecture. We also compare model performances reported in the literature. A rich literature review (65+ open-access, recent references) highlights CNN-based classifiers, transfer learning approaches and case studies in Capsicum disease detection. This work serves as a detailed reference for researchers and practitioners developing AI systems for the early detection of pepper disease.

Rice is a major staple crop that is highly susceptible to various leaf diseases, necessitating an accurate early detection method to prevent yield losses. This study proposes a hybrid approach combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for rice leaf disease classification based on digital images. The CNN is employed as a deep feature extractor, while the SVM serves as the main classifier. The dataset consists of rice leaf images categorized into four disease types: Bacterial blight, Blast, Brown spot, and Tungro. The data were divided into training and validation sets, and the CNN model was trained for 10 epochs, achieving a validation accuracy of 98.14% at the 10th epoch. The extracted CNN features were then evaluated using different SVM kernels, namely Linear, Polynomial, RBF, and Sigmoid. The experimental results show that the Sigmoid kernel achieved the best performance with an accuracy of 49%, followed by Polynomial, RBF, and Linear kernels.

Black gram, also known as urad bean, is an economically crucial crop widely cultivated in India, particularly in the central and southern regions. However, black gram is highly prone to multiple leaf diseases, resulting in considerable crop losses and economic challenges for farmers. Manual disease identification is slow and often unreliable, necessitating the development of automated disease detection methods. In this study, we propose ConViTSE, a lightweight hybrid deep learning architecture specifically designed for black gram leaf disease classification. ConViTSE integrates ConvMixer, Vision Transformer (ViT), and Squeeze and Excitation (SE) blocks to effectively extract and refine both local and global features. The model introduces Local Channel Attention Refinement (LCAR) and Global Channel Attention Refinement (GCAR) modules to enhance feature representation at different hierarchical levels. Extensive studies show that ConViTSE achieves a leading classification accuracy of 99.30% on the black gram dataset, outperforming traditional deep learning models. Furthermore, ConViTSE exhibits robust cross-domain generalization, achieving accuracies of 98.75% for rice, 98.20% for maize, and 95% for wheat, highlighting its potential for widespread adoption in precision agriculture.ConViTSE enhances disease detection accuracy while remaining computationally efficient, making it a practical tool for real-time disease management in diverse agricultural environments.

PathViT: Automated disease classification from skeletal muscle histopathology.

Proposed ResVGG-Net Model for Mango Leaf Disease Classification and Agricultural Sustainability

Brain problems lead to the loss of physical functions like speech and movement. Thus, early brain tumour diagnosis is fundamental for improving the survival of patients. Existing traditional methods follow deep neural structural design where the selection of relevant characteristics descriptors and classifiers is a main challenge. Therefore, the deep learning-based recognition of various abnormalities in the brain has been suggested. Initially, the required brain image is taken from the public dataset. The image data are then passed to the segmentation process, in which the adaptive refinement network (ARN) performs the segmentation as it is robust to outliers and can manage the intricate structure of tumours. Further, enhance the segmentation process by implementing the fitness-based flamingo search algorithm (FFSA), which optimizes the parameters in the segmentation model by efficiently exploring the search area and converging on the most favourable solutions. The resultant segmented images are sent to an ensemble convolutional neural network (CNN) with Bayesian learning (ECNN-BL) for classification. By combining several systems, ensembles can overcome overfitting issues, which lead to better generalization to new data and improved accuracy and robustness. Here, the ensemble CNN is the combination of the visual geometry group-16 (VGG16), residual neural network (Resnet), and Xception that performs effective classification. The superiority of the developed model is determined by taking a similar analysis with existing approaches. From the findings, the designed system is dependable and efficient in identifying brain diseases using the MRI images.

The electrical activity of the human heart, recorded via an electrocardiogram (ECG), is characterized by distinct waveforms such as the P wave, QRS complex, and T wave. By analyzing the duration, morphology, and intervals between these waveforms, various cardiac disorders can be identified. This study aims to develop a deep learning-based approach for the accurate classification of congenital heart disease (CHD) using ECG data. We employed convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze ECG signals, leveraging their ability to detect multiple features in time-series data. A deep learning model was developed and trained using features such as estimated peak locations, inter-peak intervals, and other ECG parameters. To address class imbalance, we applied the synthetic minority oversampling technique (SMOTE), which generates synthetic samples to balance each class. The analysis was conducted using the MIT-BIH Arrhythmia Database, enabling CHD classification based on ECG patterns. The proposed method improved classification accuracy by effectively balancing the dataset with SMOTE. Compared to conventional methods, the deep learning algorithms demonstrated robust performance in analyzing ECG data and detecting disease-related patterns, achieving superior results. This study highlights the potential of CNNs and RNNs for classifying CHD from ECG signals. By mitigating data imbalance with SMOTE, the approach enhances both accuracy and reliability. Future work will focus on validating the model with additional datasets and addressing real-world challenges such as noise handling and external validation.

Cotton diseases and pests pose significant threats to cotton production, necessitating accurate and efficient classification methods. Despite existing advanced methods, there is a research gap in utilizing both local feature extraction and global context capture for enhanced classification accuracy. Hence, this study developed and evaluated three advanced models for cotton disease and pest classification: a convolutional neural network (CNN)-based model, a Vision Transformer (ViT)-based model, and a hybrid CNN-ViT model. These models were trained on a dataset comprising eight classes of cotton diseases and pests, namely aphids, armyworm, bacterial blight, cotton boll rot, green cotton boll, healthy, powdery mildew, and target spot. The results demonstrated that the hybrid CNN-ViT model achieved the highest overall performance with an average test accuracy of 98.5%. The CNN model showed strong performance with an average accuracy of 97.9%. The ViT models, while having self-attention mechanisms to capture context and dependencies, exhibited improved performance with increased depth. The ViT model having four transformer layers outperformed the two-layer variant, achieving an average accuracy of 97.2% compared to 96.3%. The hybrid model effectively combined the strengths of CNN's local feature extraction and ViT's global feature capture, resulting in superior classification accuracy across most classes. Future research should focus on expanding the dataset to include more diverse diseases and pests and integrating the models with autonomous platforms for spraying the chemicals, thus facilitating real-world adoption and application in agricultural settings.

Kidney, liver and cyst volumes are important for diagnosis, classification and management of autosomal dominant polycystic kidney disease (ADPKD) but challenging to measure accurately and reproducibly. Here, we develop a web-based deep learning platform to automatically and robustly measure kidneys, liver and cyst volumes in ADPKD. MRI and CT scans from ADPKD patients (n=611) and participants without ADPKD (n=109) were used to train a 3D hybrid model combining U-Net and transformer elements for segmenting kidneys, liver and cysts. The model is implemented as a web-based calculator at www.traceorg.com, providing segmentation labels, volumes and Mayo Clinic Image Classification (MIC). Automatic browser anonymization of DICOM images ensures privacy. Internal validation was conducted on 70 MRIs for kidney and liver segmentations, 46 MRIs for cyst segmentations and performance was compared to 5 open access segmentation models (TotalSegmentator, MR Annotator, Kim, Woznicki and Gregory-Kline). External validation was performed on one single-center dataset (n=58), one multicenter dataset (n=73), CRISP2 (n=30) and PKD-RRC (n=115) MRIs with T2-weighted and T1-weighted images. After training on 720 participants (mean age=48±15, eGFR=74±32 ml/min/1.73m2 and htTKV=826±772ml/m), TraceOrg internal validation performance achieved high mean Dice scores of 0.97 (kidneys), 0.97 (liver), 0.93 (kidney cysts) and 0.82 (liver cysts) outperforming existing models for ADPKD. External validation showed strong performance with Dice scores of 0.92-0.94 (kidney), 0.87-0.96 (liver), 0.85 (kidney cysts) and 0.76-0.90 (liver cysts) for the single-center and 0.95 (kidney), 0.81 (kidney cysts) for the multicenter dataset. Compared to CRISP volumes measured by stereology, mean absolute percent difference was 5.3% (kidneys, n=30), 11% (kidney cysts, n=30) and 5.5% (liver, n=22). Compared to PKD-RRC (n=115), mean absolute percent difference in TKV was 4.9%. TraceOrg is a publicly available web-based tool that automatically measures kidney, liver and cyst volumes from abdominal MRI in ADPKD with high accuracy compared to manual segmentations.

Background: Cardiogenic shock often leads to hemodynamic compromise requiring immediate intervention, typically involving mechanical circulatory support (MCS). While treatment methods have advanced, there continues to be disparities in management, especially in women. While prior studies have highlighted differences in myocardial infarctions and heart failure, very few have investigated cardiogenic shock. This study aims to evaluate sex-based differences in MCS utilization and in-hospital mortality in postmenopausal women. Hypothesis: Based on trends in other cardiac diseases, we hypothesize that postmenopausal women with cardiogenic shock are less likely to receive MCS and therefore experience higher in-hospital mortality compared to men of similar age range. Methods: We analyzed the National Inpatient Sample database from 2018 to 2021, identifying adults over the age of 55 hospitalized with cardiogenic shock (International Classification of Diseases, Tenth Revision code R57.0). Primary outcomes included MCS usage determined by procedure codes for standard treatments and in-hospital mortality. Multivariable logistic regression models were created to estimate odds ratio, later adjusted for demographic data and Elixhauser comorbidity index calculated using the Van Walraven weights. Results: Out of the 155,728 patients with cardiogenic shock sampled, 43% were female. Compared to men, women tended to be slightly older with lower comorbidity index (17.4 vs 18.0) and lower MCS usage (15% vs 20%). Unadjusted regression showed women overall had 32% lower odds of receiving MCS (OR: 0.677; 95% CI: 0.657–0.699) and 22.5% higher odds of inpatient mortality (OR: 1.225; 95% CI: 1.196–1.255). After adjusting for age, Elixhauser index, race, type of insurance, and income, women still had 27.5% lower odds of receiving MCS (OR: 0.725; 95% CI: 0.702–0.748) and 17% higher odds of inpatient mortality (OR: 1.170; 95% CI: 1.141–1.199). All values were statistically significant (p-value &lt; 0.05). Conclusion: This study showed that postmenopausal women, although with slightly less comorbidities than men, still are less likely to receive MCS leading to higher inpatient mortality even after adjusting for socioeconomic factors. Combined with sex-specific disparities in other cardiac diseases, this highlights a strong necessity for investigating possible barriers to treatment at an individual and system level.

Introduction: Primary cardiac sarcomas are exceedingly rare and aggressive malignancies, with limited epidemiological data due to their low incidence. This study aims to characterize the incidence trends of cardiac sarcomas over a 21-year period using a population-based cancer registry and to assess differences in incidence by sex. Methods: We conducted a retrospective cohort study using data from the Surveillance, Epidemiology, and End Results (SEER) Program, accessed through SEER*Stat software version 8.4.5. Eligible cases were identified using the International Classification of Diseases for Oncology (ICD-O-3) site code C38.0 (Heart) and histology codes corresponding to soft tissue tumors and sarcomas, including: NOS (8800–8809), fibromatous neoplasms (8810–8839), myxomatous neoplasms (8840–8849), lipomatous neoplasms (8850–8889), myomatous neoplasms (8890–8929), complex mixed and stromal neoplasms (8930–8999), synovial-like neoplasms (9040–9049), and blood vessel tumors (9120–9169). The study period spanned from 2000 to 2021. Only first primary tumors were included, and no duplicate cases were identified. Incidence rates were age-adjusted to the 2000 U.S. standard population and stratified by sex. Temporal trends were evaluated using joinpoint regression. Results: A total of 264 cases of primary cardiac sarcomas were identified between 2000 and 2021. The overall age-adjusted incidence rate remained relatively stable throughout the study period, with no significant annual percent change observed. Males demonstrated a higher incidence rate compared to females (0.0065 vs. 0.0053 per 100,000 person-years, respectively), but this difference did not reach statistical significance (p &gt; 0.05). There were no notable shifts in the incidence of specific sarcoma subtypes over time. Conclusion: This study provides one of the most comprehensive analyses of primary cardiac sarcoma incidence trends in the U.S. to date. Despite the rarity of this malignancy, the incidence has remained stable over the past two decades. While males appear to be more affected than females, the observed sex difference is not statistically significant. Continued surveillance and larger datasets are necessary to better understand risk factors and inform early detection strategies for this rare and often fatal disease.

Objective: This study provides an up-to-date diagnosis framework for the study of emergency general surgery (EGS) patients. A final list of International Classification of Diseases, Tenth Revision (ICD-10) codes was the main outcome for the study. Codes were compared with the number codes generated by MapIT alone. Background: Since transition to ICD-10, a Delphi process to define EGS diagnoses, as originally described for the ICD, Ninth Revision (ICD-9) codeset, has not been performed. Automated mapping software (MapIT) has been utilized, with a few studies verifying the translation. Methods: Using previously defined ICD-9 EGS codes, MapIT was used to identify ICD-10 EGS codes. Review of adjacent codes in a Delphi process resulted in a finalized list of ICD-10 codes. Delphi and MapIT codes were quantified in the Nationwide Inpatient Sample to compare rates to the ICD-9 era. Results: MapIT identified 935 ICD-10 codes from 485 ICD-9 codes. Manual review identified an additional 1907 adjacent codes. In total, after the modified Delphi process, 1579 (55.6%) of manually and MapIT-identified codes were included in the final codeset. After initial mapping, 880 (55.7%) of the final codes did not automatically map through the software. MapIT codes resulted in a significantly decreased number of patient encounters in the Nationwide Inpatient Sample compared with Delphi codes in the ICD-10 era. Conclusions: The Delphi-created ICD-10 EGS codeset provides a more robust, accurate translation of the ICD-9 codes than MapIT software. This codeset can be used to inform EGS research to study and improve EGS patients’ care.