An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans.

BACKGROUND: Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-constrained settings. This study aims to propose an efficient diagnostic deep learning model specifically designed for the classification of intracranial hemorrhage in brain CT scans. METHOD: Our proposed model utilizes a combination of depthwise separable convolutions and a multi-receptive field mechanism to achieve a trade-off between performance and computational efficiency. The model was trained on RSNA datasets and validated on CQ500 dataset and PhysioNet dataset. RESULT: Through a comprehensive comparison with state-of-the-art models, our model achieves an average AUROC score of 0.952 on RSNA datasets and exhibits robust generalization capabilities, comparable to SE-ResNeXt, across other open datasets. Furthermore, the parameter count of our model is just 3 % of that of MobileNet V3. CONCLUSION: This study presents a diagnostic deep-learning model that is optimized for classifying intracranial hemorrhages in brain CT scans. The efficient characteristics make our proposed model highly promising for broader applications in medical settings.

ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram.

BACKGROUND: Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling. METHODS: This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices. RESULTS: The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]). CONCLUSION: ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.

Artificial Intelligence Model for a Distinction between Early-Stage Gastric Cancer Invasive Depth T1a and T1b.

Background: Endoscopic submucosal dissection (ESD) is a widely accepted treatment for patients with mucosa (T1a) disease without lymph node metastasis. However, the inconsistency of inspection quality of tumor staging under the standard tool combining endoscopic ultrasound (EUS) with computed tomography (CT) scanning makes it restrictive. Methods: We conducted a study using data augmentation and artificial intelligence (AI) to address the early gastric cancer (EGC) staging problem. The proposed AI model simplifies early cancer treatment by eliminating the need for ultrasound or other staging methods. We developed an AI model utilizing data augmentation and the You-Only-Look-Once (YOLO) approach. We collected a white-light image dataset of 351 stage T1a and 542 T1b images to build, test, and validate the model. An external white-light images dataset that consists of 47 T1a and 9 T1b images was then collected to validate our AI model. The result of the external dataset validation indicated that our model also applies to other peer health institutes. Results: The results of k-fold cross-validation using the original dataset demonstrated that the proposed model had a sensitivity of 85.08% and an average specificity of 87.17%. Additionally, the k-fold cross-validation model had an average accuracy rate of 86.18%; the external data set demonstrated similar validation results with a sensitivity of 82.98%, a specificity of 77.78%, and an overall accuracy of 82.14%. Conclusions: Our findings suggest that the AI model can effectively replace EUS and CT in early GC staging, with an average validation accuracy rate of 86.18% for the original dataset from Linkou Cheng Gun Memorial Hospital and 82.14% for the external validation dataset from Kaohsiung Cheng Gun Memorial Hospital. Moreover, our AI model's accuracy rate outperformed the average EUS and CT rates in previous literature (around 70%).

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bladder (KUB) radiographs are inexpensive and frequently ordered in clinical practice. Thus, it is a potential screening tool for osteoporosis. In this study, we explored the possibility of predicting the bone mineral density (BMD) and classifying high-risk patient groups using KUB radiographs. We proposed DeepDXA-KUB, a deep learning model that predicts the BMD values of the left hip and lumbar vertebrae from an input KUB image. The datasets were obtained from Taiwanese medical centers between 2006 and 2019, using 8913 pairs of KUB radiographs and DXA examinations performed within 6 months. The images were randomly divided into training and validation sets in a 4:1 ratio. To evaluate the model's performance, we computed a confusion matrix and evaluated the sensitivity, specificity, accuracy, precision, positive predictive value, negative predictive value, F1 score, and area under the receiver operating curve (AUROC). Moderate correlations were observed between the predicted and DXA-measured BMD values, with a correlation coefficient of 0.858 for the lumbar vertebrae and 0.87 for the left hip. The model demonstrated an osteoporosis detection accuracy, sensitivity, and specificity of 84.7 %, 81.6 %, and 86.6 % for the lumbar vertebrae and 84.2 %, 91.2 %, and 81 % for the left hip, respectively. The AUROC was 0.939 for the lumbar vertebrae and 0.947 for the left hip, indicating a satisfactory performance in osteoporosis screening. The present study is the first to develop a deep learning model based on KUB radiographs to predict lumbar spine and femoral BMD. Our model demonstrated a promising correlation between the predicted and DXA-measured BMD in both the lumbar vertebrae and hip, showing great potential for the opportunistic screening of osteoporosis.

A colonial serrated polyp classification model using white-light ordinary endoscopy images with an artificial intelligence model and TensorFlow chart.

In this study, we implemented a combination of data augmentation and artificial intelligence (AI) model-Convolutional Neural Network (CNN)-to help physicians classify colonic polyps into traditional adenoma (TA), sessile serrated adenoma (SSA), and hyperplastic polyp (HP). We collected ordinary endoscopy images under both white and NBI lights. Under white light, we collected 257 images of HP, 423 images of SSA, and 60 images of TA. Under NBI light, were collected 238 images of HP, 284 images of SSA, and 71 images of TA. We implemented the CNN-based artificial intelligence model, Inception V4, to build a classification model for the types of colon polyps. Our final AI classification model with data augmentation process is constructed only with white light images. Our classification prediction accuracy of colon polyp type is 94%, and the discriminability of the model (area under the curve) was 98%. Thus, we can conclude that our model can help physicians distinguish between TA, SSA, and HPs and correctly identify precancerous lesions such as TA and SSA.

Causal associations of central and peripheral risk factors with knee osteoarthritis: a longitudinal and Mendelian Randomisation study using UK Biobank data.

ABSTRACT: Our aim was to investigate relative contributions of central and peripheral mechanisms to knee osteoarthritis (OA) diagnosis and their independent causal association with knee OA. We performed longitudinal analysis using data from UK-Biobank participants. Knee OA was defined using International Classification of Diseases manual 10 codes from participants' hospital records. Central mechanisms were proxied using multisite chronic pain (MCP) and peripheral mechanisms using body mass index (BMI). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated, and proportional risk contribution (PRC) was estimated from receiver-operator-characteristic (ROC) analysis. To estimate the causal effects, we performed 2-sample multivariable Mendelian Randomisation (MR) analysis. We selected genetic instruments from the largest Genome Wide Association Study of BMI (N = 806,834) and MCP (N = 387,649) and estimated the instruments genetic associations with knee OA in the largest available dataset (62,497 cases and 333,557 control subjects). The multivariable MR was performed using modified inverse-variance weighting methods. Of the 203,410 participants, 6% developed knee OA. Both MCP (OR 1.23, 95% CI; 1.21-1.24) and BMI (1.10, 95% CI; 1.10-1.11) were associated with knee OA diagnosis. The PRC was 6.9% (95% CI; 6.7%-7.1%) for MCP and 21.9% (95% CI; 21.4%-22.5%) for BMI; the combined PRC was 38.8% (95% CI; 37.9%-39.8%). Body mass index and MCP had independent causal effects on knee OA (OR 1.76 [95% CI, 1.64-1.88] and 1.83 [95% CI, 1.54-2.16] per unit change, respectively). In conclusion, peripheral risk factors (eg, BMI) contribute more to the development of knee OA than central risk factors (eg, MCP). Peripheral and central factors are independently causal on knee OA.

Comparing Artificial Intelligence-Enabled Electrocardiogram Models in Identifying Left Atrium Enlargement and Long-term Cardiovascular Risk.

BACKGROUND: The role of P-wave in identifying left atrial enlargement (LAE) with the use of artificial intelligence (AI)-enabled electrocardiography (ECG) models is unclear. It is also unknown if AI-enabled single-lead ECG could be used as a diagnostic tool for LAE surveillance. We aimed to build AI-enabled P-wave and single-lead ECG models to identify LAE using sinus rhythm (SR) and non-SR ECGs, and compare the prognostic ability of severe LAE, defined as left atrial diameter ≥ 50 mm, assessed by AI-enabled ECG models vs echocardiography. METHODS: This retrospective study used data from 382,594 consecutive adults with paired 12-lead ECG and echocardiography performed within 2 weeks of each other at Chang Gung Memorial Hospital. UNet++ was used for P-wave segmentation. ResNet-18 was used to develop deep convolutional neural network-enabled ECG models for discriminating LAE. External validation was performed with the use of data from 11,753 patients from another hospital. RESULTS: The AI-enabled 12-lead ECG model outperformed other ECG models for classifying LAE, but the single-lead ECG models also showed excellent performance at a left atrial diameter cutoff of 50 mm. AI-enabled ECG models had excellent and fair discrimination on LAE using the SR and the non-SR data set, respectively. Severe LAE identified by AI-enabled ECG models was more predictive of future cardiovascular disease than echocardiography; however, the cumulative incidence of new-onset atrial fibrillation and heart failure was higher in patients with echocardiography-severe LAE than with AI-enabled ECG-severe LAE. CONCLUSIONS: P-Wave plays a crucial role in discriminating LAE in AI-enabled ECG models. AI-enabled ECG models outperform echocardiography in predicting new-onset cardiovascular diseases associated with severe LAE.

Genetic and Environmental Contributions of Primary Angle-Closure Glaucoma and Primary Open-Angle Glaucoma: A Nationwide Study in Taiwan.

PURPOSE: To estimate the familial risks of primary angle-closure glaucoma (PACG) and primary open-angle glaucoma (POAG) and assess the relative contributions of environmental and genetic factors to these risks. DESIGN: Retrospective, population-based cohort study. METHODS: We used the 2000-2017 Taiwan National Health Insurance Program database to construct 4,144,508 families for the 2017 population (N = 23,373,209). We used the polygenic liability model to estimate glaucoma's heritability and familial transmission. The degree of familial aggregation of glaucoma was obtained from the adjusted relative risk for individuals whose first-degree relatives had glaucoma using Cox's model. RESULTS: PACG and POAG prevalence rates for individuals whose first-degree relatives had PACG or POAG were 0.95% and 2.40%, higher than those of the general population (0.61% and 0.40%, respectively). The relative risk of PACG in individuals whose first-degree relatives had PACG was 2.44 (95% CI = 2.31-2.58). The relative risk of POAG in individuals whose first-degree relatives had POAG was 6.66 (95% CI = 6.38-6.94). The estimated contributions to PACG and POAG phenotypic variances were 19.4% and 59.6% for additive genetic variance, 19.1% and 23.2% for common environmental factors shared by family members, and 61.5% and 17.2% for nonshared environmental factors, respectively. CONCLUSIONS: These data highlight the relative importance of genetic contribution to POAG and environmental contribution to PACG. Therefore, future work may need to focus on finding more novel environmental determinants of PACG.

Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis.

OBJECTIVE: One in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease-modifying antirheumatic drugs (DMARDs) complicates surgical planning, and it is difficult to predict which patients may ultimately require surgery. We used machine learning to develop predictive models for the likelihood of undergoing an operation related to RA and which type of operation patients who require surgery undergo. METHODS: We used electronic health record data to train two extreme gradient boosting machine learning models. The first model predicted patients' probabilities of undergoing surgery ≥5 years after their initial clinic visit. The second model predicted whether patients who underwent surgery would undergo a major joint replacement versus a less intensive procedure. Predictors included demographics, comorbidities, and medication data. The primary outcome was model discrimination, measured by area under the receiver operating characteristic curve (AUC). RESULTS: We identified 5,481 patients, of whom 278 (5.1%) underwent surgery. There was no significant difference in the frequency of DMARD or steroid prescriptions between patients who did and did not have surgery, though nonsteroidal anti-inflammatory drug prescriptions were more common among patients who did have surgery (P = 0.03). The model predicting use of surgery had an AUC of 0.90 ± 0.02. The model predicting type of surgery had an AUC of 0.58 ± 0.10. CONCLUSIONS: Predictive models using clinical data have the potential to facilitate identification of patients who may undergo rheumatoid-related surgery, but not what type of procedure they will need. Integrating similar models into practice has the potential to improve surgical planning.

Causal association between subtypes of osteoarthritis and common comorbidities: A Mendelian randomisation study.

Objective: To investigate the causal association between Osteoarthritis (OA) and five comorbidities: depression, tiredness, multisite chronic pain, irritable bowel syndrome (IBS) and gout. Design: This study used two-sample Mendelian Randomisation (MR). To select the OA genetic instruments, we used data from the largest recent genome-wide association study (GWAS) of OA (GO Consortium), with a focus on OA of the knee (62,497 cases, 333,557 controls), hip (35,445 cases, 316,943 controls) and hand (20,901 cases, 282,881 controls). Genetic associations for comorbidities were selected from GWAS for depression (246,363 cases, 561,190 controls), tiredness (449,019 participants), multisite chronic pain (387,649 participants), IBS (53,400 cases, 433,201 controls) and gout (6543 cases, 456,390 controls). We performed a bidirectional MR analysis using the inverse variance weighted method, for both joint specific and overall OA. Results: Hip OA had a causal effect on multisite chronic pain (per unit change 0.02, 95% CI 0.01 to 0.04). Multisite chronic pain had a causal effect on knee (odd ratio (OR) 2.74, 95% CI 2.20 to 3.41), hip (OR 2.12, 95% CI 1.54 to 2.92), hand (OR 2.24, 95% CI 1.59 to 3.16) and overall OA (OR 2.44, 95% CI, 2.06 to 2.86). In addition, depression and tiredness had causal effects on knee and hand, but not hip, OA. Conclusions: Apart from Hip OA to multisite chronic pain, other joint OA did not have causal effects on these comorbidities. In contrast, multisite chronic pain had a causal effect on any painful OA.

Machine learning-based prediction of acute mortality in emergency department patients using twelve-lead electrocardiogram.

Background: The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients. Methods: The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were randomly divided into training, validation and testing datasets. The performance of the mortality risk prediction in this model was evaluated for various causes of death. Results: Patients who visited the ED and underwent one or more ECG examinations experienced a high incidence of 30-day mortality [18,734 (5.42%)]. The developed CNN model demonstrated high accuracy in predicting acute mortality (hazard ratio 8.50, 95% confidence interval 8.20-8.80) with areas under the receiver operating characteristic (ROC) curve of 0.84 for the 30-day mortality risk prediction models. This CNN model also demonstrated good performance in predicting one-year mortality (hazard ratio 3.34, 95% confidence interval 3.30-3.39). This model exhibited good predictive performance for 30-day mortality not only for cardiovascular diseases but also across various diseases. Conclusions: The machine learning-based ECG model utilizing CNN screens the risks for 30-day mortality. This model can complement traditional early warning scoring indexes as a useful screening tool for mortality prediction.

The impact of epilepsy and antiseizure medications on pregnancy and neonatal outcomes: A nationwide cohort study.

PURPOSE: Our objective was to assess the adverse outcomes during pregnancy, as well as for the fetus and neonates, in women with epilepsy, both with and without the use of antiseizure medications (ASMs). METHODS: A cohort of singleton pregnancies between January 1, 2004 and December 31, 2014 was identified using the Taiwan National Health Database. The pregnancies were categorized into ASM exposure, ASM nonexposure, and control (consisting of women without an epilepsy diagnosis) groups. We recorded adverse outcomes in neonates and documented pregnancy complications. The generalized estimating equation with logit link was used to estimate adjusted odds ratios. RESULTS: There were 629 singleton pregnancies in the group exposed to ASMs, 771 in the epilepsy group without ASM exposure, and 2,004,479 in the control group. Women with epilepsy had a significantly higher risk of puerperal cerebrovascular diseases (adjusted odds ratios in the exposure and nonexposure groups = 54.46 and 20.37, respectively), respiratory distress syndrome (5.1 and 2.99), mortality (3.15 and 3.22), sepsis (2.67 and 2.54), pregnancy-related hypertension (1.71 and 1.8), preeclampsia (1.87 and 1.79), cesarean delivery (1.72 and 2.15), and preterm labor (1.38 and 1.56). The use of ASMs may increase the risk of eclampsia (adjusted odds ratio = 12.27). Compared to controls, fetuses/neonates born to women with epilepsy had a higher risk of unexplained stillbirth (adjusted odds ratios in the exposure and nonexposure groups = 2.51 and 2.37, respectively), congenital anomaly (1.37 and 1.33), central nervous system malformation (3.57 and 2.25), low birth weight (1.90 and 1.97), and a low Apgar score at 5 min (2.63 and 1.3). The use of ASMs may introduce an additional risk of small for gestational age; the adjusted odds ratio was 1.51. CONCLUSION: Women with epilepsy, irrespective of their exposure to ASMs, had a slightly elevated risk of pregnancy and perinatal complications. Puerperal cerebrovascular diseases may be a hidden risk for women with epilepsy.

Automatic and human level Graf's type identification for detecting developmental dysplasia of the hip.

BACKGROUND: Developmental dysplasia of the hip (DDH) is a common congenital disorder that may lead to hip dislocation and requires surgical intervention if left untreated. Ultrasonography is the preferred method for DDH screening; however, the lack of experienced operators impedes its application in universal neonatal screening. METHODS: We developed a deep neural network tool to automatically register the five keypoints that mark important anatomical structures of the hip and provide a reference for measuring alpha and beta angles following Graf's guidelines, which is an ultrasound classification system for DDH in infants. Two-dimensional (2D) ultrasonography images were obtained from 986 neonates aged 0-6 months. A total of 2406 images from 921 patients were labeled with ground truth keypoints by senior orthopedists. RESULTS: Our model demonstrated precise keypoint localization. The mean absolute error was approximately 1 mm, and the derived alpha angle measurement had a correlation coefficient of R = 0.89 between the model and ground truth. The model achieved an area under the receiver operating characteristic curve of 0.937 and 0.974 for classifying alpha <60° (abnormal hip) and <50° (dysplastic hip), respectively. On average, the experts agreed with 96% of the inferenced images, and the model could generalize its prediction on newly collected images with a correlation coefficient higher than 0.85. CONCLUSIONS: Precise localization and highly correlated performance metrics suggest that the model can be an efficient tool for assisting DDH diagnosis in clinical settings.

Cancer survivorship and risk of pregnancy complications, adverse obstetric outcomes, and maternal morbidities in female adolescents and young adults: a nationwide population-based study from Taiwan.

BACKGROUND: Cancer treatment in female adolescent and young adult (AYA) cancer survivors (i.e., those diagnosed between 15 and 39 years of age) may adversely affect multiple bodily functions, including the reproductive system. METHODS: We initially assembled a retrospective, nationwide population-based cohort study by linking data from two nationwide Taiwanese data sets. We subsequently identified first pregnancies and singleton births to AYA cancer survivors (2004-2018) and select AYA without a previous cancer diagnosis matched to AYA cancer survivors for maternal age and infant birth year. RESULTS: The study cohort consisted of 5151 and 51,503 births to AYA cancer survivors and matched AYA without a previous cancer diagnosis, respectively. The odds for overall pregnancy complications (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.01-1.18) and overall adverse obstetric outcomes (OR, 1.07; 95% CI, 1.01-1.13) were significantly increased in survivors compared with matched AYA without a previous cancer diagnosis. Specifically, cancer survivorship was associated with an increased risk of preterm labour, labour induction, and threatened abortion or threatened labour requiring hospitalisation. CONCLUSIONS: AYA cancer survivors are at increased risk for pregnancy complications and adverse obstetric outcomes. Efforts to integrate individualised care into clinical guidelines for preconception and prenatal care should be thoroughly explored.

The effect of paternal psoriasis on neonatal outcomes: a nationwide population-based study.

Background: Psoriasis is a chronic autoimmune disease involving both environmental and genetic risk factors. Maternal psoriasis often results in poor pregnancies that influence both mothers and newborns. However, the influence of paternal psoriasis on the newborn remains unknown. The aim of this study was to investigate whether paternal psoriasis is associated with increased risk of adverse neonatal outcomes, within a nationwide population-based data setting. Methods: Singleton pregnancies were identified in the Taiwan National Health Insurance database and National Birth Registry between 2004-2011 and classified into four study groups according to whether mothers and spouses had psoriasis (paternal(-)/maternal(-), paternal(+)/maternal(-), paternal(-)/maternal(+), and paternal(+)/maternal(+)). Data were analyzed retrospectively. Adjusted odds ratios (aOR) or hazard ratios (aHR) were calculated to evaluate the risk of neonatal outcomes between groups. Results: A total of 1,498,892 singleton pregnancies were recruited. Newborns of fathers with psoriasis but not of mothers with psoriasis were associated with an aHR (95% CI) of 3.69 (1.65-8.26) for psoriasis, 1.13 (1.06-1.21) for atopic dermatitis and 1.05 (1.01-1.10) for allergic rhinitis. Newborns of mothers with psoriasis but not of fathers with psoriasis were associated with an aOR (95% CI) of 1.26 (1.12-1.43) for low birth weight (<2500 g) and 1.64 (1.10-2.43) for low Apgar scores, and an aHR of 5.70 (2.71-11.99) for psoriasis. Conclusion: Newborns of fathers with psoriasis are associated with significantly higher risk of developing atopic dermatitis, allergic rhinitis and psoriasis. Caution is advised for adverse neonatal outcomes when either or both parents have psoriasis.

Can a Deep Learning Algorithm Improve Detection of Occult Scaphoid Fractures in Plain Radiographs? A Clinical Validation Study.

BACKGROUND: Occult scaphoid fractures on initial radiographs of an injury are a diagnostic challenge to physicians. Although artificial intelligence models based on the principles of deep convolutional neural networks (CNN) offer a potential method of detection, it is unknown how such models perform in the clinical setting. QUESTIONS/PURPOSES: (1) Does CNN-assisted image interpretation improve interobserver agreement for scaphoid fractures? (2) What is the sensitivity and specificity of image interpretation performed with and without CNN assistance (as stratified by type: normal scaphoid, occult fracture, and apparent fracture)? (3) Does CNN assistance improve time to diagnosis and physician confidence level? METHODS: This survey-based experiment presented 15 scaphoid radiographs (five normal, five apparent fractures, and five occult fractures) with and without CNN assistance to physicians in a variety of practice settings across the United States and Taiwan. Occult fractures were identified by follow-up CT scans or MRI. Participants met the following criteria: Postgraduate Year 3 or above resident physician in plastic surgery, orthopaedic surgery, or emergency medicine; hand fellows; and attending physicians. Among the 176 invited participants, 120 completed the survey and met the inclusion criteria. Of the participants, 31% (37 of 120) were fellowship-trained hand surgeons, 43% (52 of 120) were plastic surgeons, and 69% (83 of 120) were attending physicians. Most participants (73% [88 of 120]) worked in academic centers, whereas the remainder worked in large, urban private practice hospitals. Recruitment occurred between February 2022 and March 2022. Radiographs with CNN assistance were accompanied by predictions of fracture presence and gradient-weighted class activation mapping of the predicted fracture site. Sensitivity and specificity of the CNN-assisted physician diagnoses were calculated to assess diagnostic performance. We calculated interobserver agreement with the Gwet agreement coefficient (AC1). Physician diagnostic confidence was estimated using a self-assessment Likert scale, and the time to arrive at a diagnosis for each case was measured. RESULTS: Interobserver agreement among physicians for occult scaphoid radiographs was higher with CNN assistance than without (AC1 0.42 [95% CI 0.17 to 0.68] versus 0.06 [95% CI 0.00 to 0.17], respectively). No clinically relevant differences were observed in time to arrive at a diagnosis (18 ± 12 seconds versus 30 ± 27 seconds, mean difference 12 seconds [95% CI 6 to 17]; p < 0.001) or diagnostic confidence levels (7.2 ± 1.7 seconds versus 6.2 ± 1.6 seconds; mean difference 1 second [95% CI 0.5 to 1.3]; p < 0.001) for occult fractures. CONCLUSION: CNN assistance improves physician diagnostic sensitivity and specificity as well as interobserver agreement for the diagnosis of occult scaphoid fractures. The differences observed in diagnostic speed and confidence is likely not clinically relevant. Despite these improvements in clinical diagnoses of scaphoid fractures with the CNN, it is unknown whether development and implementation of such models is cost effective. LEVEL OF EVIDENCE: Level II, diagnostic study.

Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction.

Background: Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis. Methods: This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF <40%, using original ECG signals or transformed images from 190,359 patients with paired ECG and echocardiogram within 14 days. The 190,359 patients were divided into a training set of 133,225 and a validation set of 57,134. The accuracy of recognizing LVSD and subsequent mortality predictions were tested using ECGs from 190,316 patients with paired data. Of these 190,316 patients, we further selected 49,564 patients with multiple echocardiographic data to predict LVSD incidence. We additionally used data from 1,194,982 patients who underwent ECG only to assess mortality prognostication. External validation was performed using data of 91,425 patients from Tri-Service General Hospital, Taiwan. Results: The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% women), and 8,216 patients (4.3%) had LVSD. The median follow-up period was 3.9 years (interquartile range 1.5-7.9 years). The area under the receiver-operating characteristic curve (AUROC), sensitivity, and specificity of the signal-based DNN (DNN-signal) to identify LVSD were 0.95, 0.91, and 0.86, respectively. DNN signal-predicted LVSD was associated with age- and sex-adjusted hazard ratios (HRs) of 2.57 (95% confidence interval [CI], 2.53-2.62) for all-cause mortality and 6.09 (5.83-6.37) for cardiovascular mortality. In patients with multiple echocardiograms, a positive DNN prediction in patients with preserved LVEF was associated with an adjusted HR (95% CI) of 8.33 (7.71 to 9.00) for incident LVSD. Signal- and image-based DNNs performed equally well in the primary and additional datasets. Conclusion: Using DNNs, ECG becomes a low-cost, clinically feasible tool to screen LVSD and facilitate accurate prognostication.

Diagnostic Evaluation Using Salivary Gland Ultrasonography in Primary Sjögren's Syndrome.

The purpose of this study is to investigate the clinical manifestations in patients with early primary Sjögren's syndrome (pSS) based on the severity score found by salivary gland ultrasonography. Thirty-five newly diagnosed patients with early pSS were enrolled and divided into mild (score 0-1) and severe (score 2-3) groups according to the salivary gland ultrasonography grade (SGUS) scores at baseline. Clinical evaluation, ESSPRI and ESSDAI index values, sicca symptoms of the mouth, salivary capacity, and serum autoantibodies and cytokines were investigated. The mean age of pSS patients at diagnosis was 49.9 ± 11.9 years, and the mean duration of sicca symptoms was 0.58 years. ESSPRI (EULAR Sjögren's syndrome patient report index) and ESSDAI (EULAR Sjögren's syndrome disease index) scores were 15.97 and 4.77, respectively. Clinical manifestations, including the low production of saliva and autoantibody production, such as antinuclear antibodies, rheumatoid factor, and anti-SSA antibody, were found. A higher prevalence of rheumatoid factor (p = 0.0365) and antinuclear antibody (p = 0.0063) and a higher elevation of total IgG (p = 0.0365) were found in the severe group than in the mild group. In addition, the elevated titer of IL-25 was detected in the severe group than in the mild group. This observation indicated that salivary gland ultrasonography grade (SGUS) scans may help physicians diagnose pSS and the elevated titer of IL-25 in patients may be implicated in the pathogenesis of pSS.

Cardiovascular outcomes in patients with atrial fibrillation concomitantly treated with antiarrhythmic drugs and non-vitamin k antagonist oral anticoagulants.

AIMS: Limited data compared antiarrhythmic drugs (AADs) with concomitant non-vitamin K antagonist oral anticoagulants in atrial fibrillation patients, hence the aim of the study. METHODS AND RESULTS: National health insurance database were retrieved during 2012-17 for study. We excluded patients not taking AADs, bradycardia, heart block, heart failure admission, mitral stenosis, prosthetic valve, incomplete demographic data, and follow-up <3 months. Outcomes were compared in Protocol 1, dronedarone vs. non-dronedarone; Protocol 2, dronedarone vs. amiodarone; and Protocol 3, dronedarone vs. propafenone. Outcomes were acute myocardial infarction (AMI), ischaemic stroke/systemic embolism, intracranial haemorrhage (ICH), major bleeding, cardiovascular death, all-cause mortality, and major adverse cardiovascular event (MACE) (including AMI, ischaemic stroke, and cardiovascular death). In Protocol 1, 2298 dronedarone users and 6984 non-dronedarone users (amiodarone = 4844; propafenone = 1914; flecainide = 75; sotalol = 61) were analysed. Dronedarone was associated with lower ICH (HR = 0.61, 95% CI = 0.38-0.99, P = 0.0436), cardiovascular death (HR = 0.24, 95% CI = 0.16-0.37, P < 0.0001), all-cause mortality (HR = 0.33, 95% CI = 0.27-0.42, P < 0.0001), and MACE (HR = 0.56, 95% CI = 0.45-0.70, P < 0.0001). In Protocol 2, 2231 dronedarone users and 6693 amiodarone users were analysed. Dronedarone was associated with significantly lower ICH (HR = 0.53, 95%=CI 0.33-0.84, P = 0.0078), cardiovascular death (HR = 0.20, 95% CI = 0.13-0.31, P < 0.0001), all-cause mortality (HR 0.27, 95% CI 0.22-0.34, P < 0.0001), and MACE (HR = 0.53, 95% CI = 0.43-0.66, P < 0.0001), compared with amiodarone. In Protocol 3, 812 dronedarone users and 2436 propafenone users were analysed. There were no differences between two drugs for primary and secondary outcomes. CONCLUSION: The use of dronedarone with NOACs was associated with cardiovascular benefits in an Asian population, compared with non-dronedarone AADs and amiodarone.

Sensitive label-free detection of the biomarker phosphorylated tau-217 protein in Alzheimer's disease using a graphene-based solution-gated field effect transistor.

Alzheimer's disease (AD) is generally diagnosed using advanced imaging, but recent research suggests early screening using biomarkers in peripheral blood is feasible; among them, plasma tau proteins phosphorylated at threonine 231, threonine 181, and threonine 217 (p-tau217) are potential targets. A recent study indicates that the p-tau217 protein is the most efficacious biomarker. However, a clinical study found a pg/ml threshold for AD screening beyond standard detection methods. A biosensor with high sensitivity and specificity p-tau217 detection has not yet been reported. In this study, we developed a label-free solution-gated field effect transistor (SGFET)-based biosensor featuring a graphene oxide/graphene (GO/G) layered composite. The top layer of bilayer graphene grown using chemical vapor deposition was functionalized with oxidative groups serving as active sites for forming covalent bonds with the biorecognition element (antibodies); the bottom G could act as a transducer to respond to the attachment of the target analytes onto the top GO conjugated with the biorecognition element via π-π interactions between the GO and G layers. With this unique atomically layered G composite, we obtained a good linear electrical response in the Dirac point shift to p-tau217 protein concentrations in the range of 10 fg/ml to 100 pg/ml. The biosensor exhibited a high sensitivity of 18.6 mV/decade with a high linearity of 0.991 in phosphate-buffered saline (PBS); in human serum albumin, it showed approximately 90% of the sensitivity (16.7 mV/decade) in PBS, demonstrating high specificity. High stability of the biosensor was also displayed in this study.