AI in the ED: Assessing the efficacy of GPT models vs. physicians in medical score calculation.

BACKGROUND AND AIMS: Artificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART score, to guide clinical decision-making. This study aims to evaluate the proficiency of GPT-3.5 and GPT-4 against experienced ED physicians in calculating five commonly used medical scores. METHODS: This retrospective study analyzed data from 150 patients who visited the ED over one week. Both AI models and two human physicians were tasked with calculating scores for NIH Stroke Scale, Canadian Syncope Risk Score, Alvarado Score for Acute Appendicitis, Canadian CT Head Rule, and HEART Score. Cohen's Kappa statistic and AUC values were used to assess inter-rater agreement and predictive performance, respectively. RESULTS: The highest level of agreement was observed between the human physicians (Kappa = 0.681), while GPT-4 also showed moderate to substantial agreement with them (Kappa values of 0.473 and 0.576). GPT-3.5 had the lowest agreement with human scorers. These results highlight the superior predictive performance of human expertise over the currently available automated systems for this specific medical outcome. Human physicians achieved a higher ROC-AUC on 3 of the 5 scores, but none of the differences were statistically significant. CONCLUSIONS: While AI models demonstrated some level of concordance with human expertise, they fell short in emulating the complex clinical judgments that physicians make. The study suggests that current AI models may serve as supplementary tools but are not ready to replace human expertise in high-stakes settings like the ED. Further research is needed to explore the capabilities and limitations of AI in emergency medicine.

Advancing Medical Practice with Artificial Intelligence: ChatGPT in Healthcare.

BACKGROUND: Advancements in artificial intelligence (AI) and natural language processing (NLP) have led to the development of language models such as ChatGPT. These models have the potential to transform healthcare and medical research. However, understanding their applications and limitations is essential. OBJECTIVES: To present a view of ChatGPT research and to critically assess ChatGPT's role in medical writing and clinical environments. METHODS: We performed a literature review via the PubMed search engine from 20 November 2022, to 23 April 2023. The search terms included ChatGPT, OpenAI, and large language models. We included studies that focused on ChatGPT, explored its use or implications in medicine, and were original research articles. The selected studies were analyzed considering study design, NLP tasks, main findings, and limitations. RESULTS: Our study included 27 articles that examined ChatGPT's performance in various tasks and medical fields. These studies covered knowledge assessment, writing, and analysis tasks. While ChatGPT was found to be useful in tasks such as generating research ideas, aiding clinical reasoning, and streamlining workflows, limitations were also identified. These limitations included inaccuracies, inconsistencies, fictitious information, and limited knowledge, highlighting the need for further improvements. CONCLUSIONS: The review underscores ChatGPT's potential in various medical applications. Yet, it also points to limitations that require careful human oversight and responsible use to improve patient care, education, and decision-making.

Towards AI-Augmented Clinical Decision-Making: An Examination of ChatGPT's Utility in Acute Ulcerative Colitis Presentations.

This study explores the potential of OpenAI's ChatGPT as a decision support tool for acute ulcerative colitis presentations in the setting of an emergency department. We assessed ChatGPT's performance in determining disease severity using TrueLove and Witts criteria and the necessity of hospitalization for patients with ulcerative colitis, comparing results with those of expert gastroenterologists. Of 20 cases, ChatGPT's assessments were found to be 80% consistent with gastroenterologist evaluations and indicated a high degree of reliability. This suggests that ChatGPT could provide as a clinical decision support tool in assessing acute ulcerative colitis, serving as an adjunct to clinical judgment.

Performance of BeBack Crossing Catheter in Chronic Total Occlusion in Patients With Chronic Limb-Threatening Ischemia: A Single-Center Experience.

PURPOSE: To describe a single-center experience in the treatment of chronic limb-threatening ischemia (CLTI) with the application of BeBack catheter (Bentley InnoMed, Germany) in patients with arterial chronic total occlusion (CTO). MATERIALS AND METHODS: A retrospective review of patients who underwent limb revascularizations using the BeBack catheter between 2015 and 2022. All patients had an initial failed attempt using a traditional guidewire and catheter technique. Technical success was considered whenever a successful re-entry or lesion crossing using the study device was achieved. Procedural success was defined as recanalization of the occluded artery with residual stenosis of less than 30%, and improvement in ankle-brachial index (ABI) after 24 hours. A Rutherford score was assigned to each limb and affected anatomical segments and lesion length were documented. Procedural access sites and complications were noted. RESULTS: The study included 72 patients who underwent 78 procedures using the BeBack crossing catheter. Procedural success was achieved in 91% of cases, with a technical success rate of 92.3%. The most frequently involved occluded segments were the femoral and popliteal arteries. The average ABI improved from 0.59 to 0.95 after the procedure. The most used access site was the contralateral femoral, and the BeBack catheter was employed on 85 occasions. Only 1 patient suffered a severe immediate adverse effect, and during the 30-day follow-up period, 2 patients needed reintervention. Unfortunately, 3 patients died during the follow-up period. CONCLUSION: The BeBack catheter offers a viable option for the treatment of patients with chronic total occlusion, with high procedural success and a low complication rate. CLINICAL IMPACT: The BeBack catheter presents a notable advancement for clinicians managing chronic limb-threatening ischemia (CLTI) and arterial chronic total occlusion (CTO), showcasing over 90% procedural and technical success rates in this study. Its adept ability to navigate and recanalize occluded segments provides a robust alternative, especially when traditional techniques falter. This innovation may chane clinical strategies in vascular interventions, offering an efficient and reliable option, thereby potentially enhancing patient outcomes in limb revascularizations.

Effect of magnet strength on fetal brain biometry - a single-center retrospective MRI-based cohort study.

PURPOSE: Abnormal fetal brain measurements might affect clinical management and parental counseling. The effect of between-field-strength differences was not evaluated in quantitative fetal brain imaging until now. Our study aimed to compare fetal brain biometry measurements in 3.0 T with 1.5 T scanners. METHODS: A retrospective cohort of 1150 low-risk fetuses scanned between 2012 and 2021, with apparently normal brain anatomy, were retrospectively evaluated for biometric measurements. The cohort included 1.5 T (442 fetuses) and 3.0 T scans (708 fetuses) of populations with comparable characteristics in the same tertiary medical center. Manually measured biometry included bi-parietal, fronto-occipital and trans-cerebellar diameters, length of the corpus-callosum, vermis height, and width. Measurements were then converted to centiles based on previously reported biometric reference charts. The 1.5 T centiles were compared with the 3.0 T centiles. RESULTS: No significant differences between centiles of bi-parietal diameter, trans-cerebellar diameter, or length of the corpus callosum between 1.5 T and 3.0 T scanners were found. Small absolute differences were found in the vermis height, with higher centiles in the 3.0 T, compared to the 1.5 T scanner (54.6th-centile, vs. 39.0th-centile, p < 0.001); less significant differences were found in vermis width centiles (46.9th-centile vs. 37.5th-centile, p = 0.03). Fronto-occipital diameter was higher in 1.5 T than in the 3.0 T scanner (66.0th-centile vs. 61.8th-centile, p = 0.02). CONCLUSIONS: The increasing use of 3.0 T MRI for fetal imaging poses a potential bias when using 1.5 T-based charts. We elucidate those biometric measurements are comparable, with relatively small between-field-strength differences, when using manual biometric measurements. Small inter-magnet differences can be related to higher spatial resolution with 3 T scanners and may be substantial when evaluating small brain structures, such as the vermis.

Cardiovascular Risk Factors and Physical Fitness Among Subjects with Asymptomatic Colonic Diverticulosis.

BACKGROUND: The association between diverticular disease and atherosclerotic cardiovascular disease (ASCVD) has been demonstrated previously, mainly in symptomatic subjects. AIMS: To evaluate 10 years cardiovascular risk, exercise performance and association to ASCVD among subjects with asymptomatic diverticulosis. METHODS: A retrospective cross-sectional cohort of self-referred participants in a medical screening program, who underwent a screening colonoscopy. Demographics, clinical and laboratory variables, ASCVD score, and metabolic equivalents (METs) during treadmill stress test were compared between subjects with and without diverticulosis as diagnosed on screening colonoscopy. RESULTS: 4586 participants underwent screening colonoscopy; 799 (17.4%) had diverticulosis. Among 50-69 yo participants, diverticulosis subjects had a higher ASCVD score compared to non-diverticulosis subjects. Exercise performance was comparable between the groups, across all age groups. Using logistic regression analysis, advanced age group (50-59 yo Adjusted odds ratio (AOR) [95% confidence interval (CI)] 2.57 (1.52-4.34), p < 0.001; 60-69 yo, AOR 2.87 (2.09-3.95), p < 0.001; ≥ 70 yo AOR 4.81 (3.23-7.15), p < 0.001; compared to < 50 yo age group), smoking [AOR 1.27 (1.05-1.55), p = 0.016], HTN [AOR 1.27 (1.03-1.56), p = 0.022], obesity [AOR 1.36 (1.06-1.74), p = 0.014] and male sex [AOR 1.29 (1.02-1.64), p = 0.036] were associated with diverticular detection during screening colonoscopy. Among males, achieving METs score ≥ 10 was inversely associated with diverticular detection during screening colonoscopy [AOR 0.64 (0.43-0.95), p = 0.027]. CONCLUSIONS: Ten years probability for ASCVD estimated by the ASCVD score is higher among subjects with asymptomatic diverticulosis compared to subjects without diverticulosis. Improved exercise performance is demonstrated for the first time to correlate with decreased probability for diverticular disease in screening colonoscopy.

Retrograde tibial access for revascularization of chronic total occlusions in patients with chronic limb-threatening ischemia.

PURPOSE: To evaluate tibial single access in treatment of chronic total occlusions (CTO) in patients with ipsilateral chronic-limb ischemia (CLTI). MATERIALS AND METHODS: In this retrospective study, data was collected on patients treated for ipsilateral CTO via a tibial artery access between March 2017 and March 2021. Fifty-nine limbs in 57 patients, (42 men, average age 73 years; range 47-96) were treated. Patient's symptoms were classified in accordance with the Rutherford category. The end points were freedom from major amputation and the need for reintervention up to 1 year of follow up. RESULTS: Out of the 59 treated limbs, technical success was achieved in 57 (97%). The treated multilevel segments involved 5 common and 12 external iliac arteries, 23 common and 37 superficial femoral arteries, 23 femoropopliteal segments, 14 popliteal arteries, and 4 bypasses. Mean length of occlusion was 186 mm (range 7-670). Rutherford classification of the treated limbs was category 5 and 6 in 45 patients and category 4 in 14 patients. Three procedural complications occurred and were successfully treated during the same procedure. No immediate post-procedural complication was encountered. Median follow-up was 13 months (range 1-45.3). Reintervention was required in 9 limbs, after an average of 6 months. One year free from amputation rate was 91.2%. CONCLUSIONS: Single access via the ipsilateral tibial artery can be a useful, effective, and safe approach for treating CTO in CLTI patients.

Ultrasound-Tomographic Image Fusion - A Novel Tool for Follow up After Acute Complicated Appendicitis.

BACKGROUND: Computerized tomography (CT) is an integral part of the follow-up and decision-making process in complicated acute appendicitis (AA) treated non-operatively. However, repeated CT scans are costly and cause radiation exposure. Ultrasound-tomographic image fusion is a novel tool that integrates CT images to an Ultrasound (US) machine, thus allowing accurate assessment of the healing process compared to CT on presentation. In this study, we aimed to assess the feasibility of US-CT fusion as part of the management of appendicitis. MATERIALS AND METHODS: We retrospectively collected data of consecutive patients with complicated AA managed non-operatively and followed up with US Fusion for clinical decision-making. Patients demographics, clinical data, and follow-up outcomes were extracted and analyzed. RESULTS: Overall, 19 patients were included. An index Fusion US was conducted in 13 patients (68.4%) during admission, while the rest were performed as part of an ambulatory follow-up. Nine patients (47.3%) had more than 1 US Fusion performed as part of their follow-up, and 3 patients underwent a third US Fusion. Eventually, 5 patients (26.3%) underwent elective interval appendectomy based on the outcomes of the US Fusion, due to a non-resolution of imaging findings and ongoing symptoms. In 10 patients (52.6%), there was no evidence of an abscess in the repeated US Fusion, while in 3 patients (15.8%), it significantly diminished to less than 1 cm in diameter. CONCLUSION: Ultrasound-tomographic image fusion is feasible and can play a significant role in the decision-making process for the management of complicated AA.

Artificial intelligence for identification of focal lesions in intraoperative liver ultrasonography.

PURPOSE: Intraoperative ultrasonography (IOUS) of the liver is a crucial adjunct in every liver resection and may significantly impact intraoperative surgical decisions. However, IOUS is highly operator dependent and has a steep learning curve. We describe the design and assessment of an artificial intelligence (AI) system to identify focal liver lesions in IOUS. METHODS: IOUS images were collected during liver resections performed between November 2020 and November 2021. The images were labeled by radiologists and surgeons as normal liver tissue versus images that contain liver lesions. A convolutional neural network (CNN) was trained and tested to classify images based on the labeling. Algorithm performance was tested in terms of area under the curves (AUCs), accuracy, sensitivity, specificity, F1 score, positive predictive value, and negative predictive value. RESULTS: Overall, the dataset included 5043 IOUS images from 16 patients. Of these, 2576 were labeled as normal liver tissue and 2467 as containing focal liver lesions. Training and testing image sets were taken from different patients. Network performance area under the curve (AUC) was 80.2 ± 2.9%, and the overall classification accuracy was 74.6% ± 3.1%. For maximal sensitivity of 99%, the classification specificity is 36.4 ± 9.4%. CONCLUSIONS: This study provides for the first time a proof of concept for the use of AI in IOUS and show that high accuracy can be achieved. Further studies using high volume data are warranted to increase accuracy and differentiate between lesion types.

Alerting on mortality among patients discharged from the emergency department: a machine learning model.

OBJECTIVES: Physicians continuously make tough decisions when discharging patients. Alerting on poor outcomes may help in this decision. This study evaluates a machine learning model for predicting 30-day mortality in emergency department (ED) discharged patients. METHODS: We retrospectively analysed visits of adult patients discharged from a single ED (1/2014-12/2018). Data included demographics, evaluation and treatment in the ED, and discharge diagnosis. The data comprised of both structured and free-text fields. A gradient boosting model was trained to predict mortality within 30 days of release from the ED. The model was trained on data from the years 2014-2017 and validated on data from the year 2018. In order to reduce potential end-of-life bias, a subgroup analysis was performed for non-oncological patients. RESULTS: Overall, 363 635 ED visits of discharged patients were analysed. The 30-day mortality rate was 0.8%. A majority of the mortality cases (65.3%) had a known oncological disease. The model yielded an area under the curve (AUC) of 0.97 (95% CI 0.96 to 0.97) for predicting 30-day mortality. For a sensitivity of 84% (95% CI 0.81 to 0.86), this model had a false positive rate of 1:20. For patients without a known malignancy, the model yielded an AUC of 0.94 (95% CI 0.92 to 0.95). CONCLUSIONS: Although not frequent, patients may die following ED discharge. Machine learning-based tools may help ED physicians identify patients at risk. An optimised decision for hospitalisation or palliative management may improve patient care and system resource allocation.

Ulcer severity grading in video capsule images of patients with Crohn's disease: an ordinal neural network solution.

BACKGROUND AND AIMS: Capsule endoscopy (CE) is an important modality for diagnosis and follow-up of Crohn's disease (CD). The severity of ulcers at endoscopy is significant for predicting the course of CD. Deep learning has been proven accurate in detecting ulcers on CE. However, endoscopic classification of ulcers by deep learning has not been attempted. The aim of our study was to develop a deep learning algorithm for automated grading of CD ulcers on CE. METHODS: We retrospectively collected CE images of CD ulcers from our CE database. In experiment 1, the severity of each ulcer was graded by 2 capsule readers based on the PillCam CD classification (grades 1-3 from mild to severe), and the inter-reader variability was evaluated. In experiment 2, a consensus reading by 3 capsule readers was used to train an ordinal convolutional neural network (CNN) to automatically grade images of ulcers, and the resulting algorithm was tested against the consensus reading. A pretraining stage included training the network on images of normal mucosa and ulcerated mucosa. RESULTS: Overall, our dataset included 17,640 CE images from 49 patients; 7391 images with mucosal ulcers and 10,249 normal images. A total of 2598 randomly selected pathologic images were further graded from 1 to 3 according to ulcer severity in the 2 different experiments. In experiment 1, overall inter-reader agreement occurred for 31% of the images (345 of 1108) and 76% (752 of 989) for distinction of grades 1 and 3. In experiment 2, the algorithm was trained on 1242 images. It achieved an overall agreement for consensus reading of 67% (166 of 248) and 91% (158 of 173) for distinction of grades 1 and 3. The classification accuracy of the algorithm was 0.91 (95% confidence interval, 0.867-0.954) for grade 1 versus grade 3 ulcers, 0.78 (95% confidence interval, 0.716-0.844) for grade 2 versus grade 3, and 0.624 (95% confidence interval, 0.547-0.701) for grade 1 versus grade 2. CONCLUSIONS: CNN achieved high accuracy in detecting severe CD ulcerations. CNN-assisted CE readings in patients with CD can potentially facilitate and improve diagnosis and monitoring in these patients.

Deep learning for noninvasive liver fibrosis classification: A systematic review.

BACKGROUND AND AIMS: While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging. METHODS: Embase, MEDLINE, Web of Science, and IEEE Xplore databases were used to identify studies that reported on the accuracy of DL for classification of liver fibrosis on noninvasive imaging. The search keywords were "liver or hepatic," "fibrosis or cirrhosis," and "neural or DL networks." Risk of bias and applicability were evaluated using the QUADAS-2 tool. RESULTS: Sixteen studies were retrieved. Imaging modalities included ultrasound (n = 10), computed tomography (n = 3), and magnetic resonance imaging (n = 3). The studies analyzed a total of 40 405 radiological images from 15 853 patients. All but two of the studies were retrospective. In most studies the "ground truth" reference was the METAVIR score for pathological staging (n = 9.56%). The majority of the studies reported an accuracy >85% when compared to histopathology. Fourteen studies (87.5%) had a high risk of bias and concerns regarding applicability. CONCLUSIONS: Deep learning has the potential to play an emerging role in liver fibrosis classification. Yet, it is still limited by a relatively small number of retrospective studies. Clinicians should facilitate the use of this technology by sharing databases and standardized reports. This may optimize the noninvasive evaluation of liver fibrosis on a large scale.

Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis.

BACKGROUND: In the past decade, deep learning has revolutionized medical image processing. This technique may advance laparoscopic surgery. Study objective was to evaluate whether deep learning networks accurately analyze videos of laparoscopic procedures. METHODS: Medline, Embase, IEEE Xplore, and the Web of science databases were searched from January 2012 to May 5, 2020. Selected studies tested a deep learning model, specifically convolutional neural networks, for video analysis of laparoscopic surgery. Study characteristics including the dataset source, type of operation, number of videos, and prediction application were compared. A random effects model was used for estimating pooled sensitivity and specificity of the computer algorithms. Summary receiver operating characteristic curves were calculated by the bivariate model of Reitsma. RESULTS: Thirty-two out of 508 studies identified met inclusion criteria. Applications included instrument recognition and detection (45%), phase recognition (20%), anatomy recognition and detection (15%), action recognition (13%), surgery time prediction (5%), and gauze recognition (3%). The most common tested procedures were cholecystectomy (51%) and gynecological-mainly hysterectomy and myomectomy (26%). A total of 3004 videos were analyzed. Publications in clinical journals increased in 2020 compared to bio-computational ones. Four studies provided enough data to construct 8 contingency tables, enabling calculation of test accuracy with a pooled sensitivity of 0.93 (95% CI 0.85-0.97) and specificity of 0.96 (95% CI 0.84-0.99). Yet, the majority of papers had a high risk of bias. CONCLUSIONS: Deep learning research holds potential in laparoscopic surgery, but is limited in methodologies. Clinicians may advance AI in surgery, specifically by offering standardized visual databases and reporting.

Normal-range emergency department serum phosphorus levels and all-cause mortality.

PURPOSE OF THE STUDY: Hypophosphataemia and hyperphosphataemia are frequently encountered in hospitalised patients and are associated with significant clinical consequences. However, the prognostic value of normal-range phosphorus levels on all-cause mortality and hospitalisations is not well established. Therefore, we examined the association between normal-range phosphorus levels, all-cause mortality and hospitalisations in patients presenting to the emergency department of a tertiary medical centre in Israel. STUDY DESIGN: A retrospective analysis of patients presenting to the Chaim Sheba Medical Center emergency department between 2012 and 2018. The cohort was divided into quartiles based on emergency department phosphorus levels: 'very-low-normal' (p ≥ 2 mg/dL and p ≤ 2.49 mg/dL), 'low-normal' (p ≥ 2.5 mg/dL and p ≤ 2.99 mg/dL), 'high-normal' (p≥  3 mg/dL and p≤3.49 mg/dL) and 'very-high-normal' (p ≥  3.5 mg/dL and p ≤ 4 mg/dL). We analysed the association between emergency department phosphorus levels, hospitalisation rate and 30-day and 90-day all-cause mortality. RESULTS: Our final analysis included 223 854 patients with normal-range phosphorus levels. Patients with 'very-low-normal' phosphorus levels had the highest mortality rate. Compared with patients with 'high-normal' phosphorus levels, patients with 'very-low-normal' levels had increased 30-day all-cause mortality (OR 1.3, 95% CI 1.1 to 1.4, p<0.001), and increased 90-day all-cause mortality (OR 1.2, 95% CI 1.1 to 1.3, p<0.001). Lower serum phosphorus levels were also associated with a higher hospitalisation rate, both for the internal medicine and general surgery wards (p<0.001). CONCLUSIONS: Lower phosphorus levels, within the normal range, are associated with higher 30-day and 90-day all-cause mortality and hospitalisation rate.

Management of Acute Appendicitis during the COVID-19 Pandemic: A Single Tertiary Center Experience.

BACKGROUND: The coronavirus disease-2019 (COVID-19) outbreak had an effect on healthcare. OBJECTIVES: To evaluate the presentation and management of patients with acute appendicitis. METHODS: A retrospective study was conducted of all patients presenting with acute appendicitis to the emergency department of a large tertiary center during March and April 2020. Clinical features, diagnostic workup, and management were compared. RESULTS: Seventy-four patients presented with acute appendicitis during the pandemic compared to 60 patients during the same time the year before. There were no significant differences in patient demographics: age (P = 0.65), gender (P = 0.73), smoking status (P = 0.48). During COVID-19 patients were more likely to complain of right lower quadrant pain (100% vs. 78.3%, P < 0.01). Rates of surgical treatment was similar (83.8% vs. 81.7%, P = 1); mean operative time was longer during COVID-19 (63 ± 23 vs. 52 ± 26 minutes, P = 0.03). There were no significant differences in intra-operative findings including the presence of appendiceal perforation (16.3% vs. 14.5%, P = 0.8), abscess (6.1% vs. 9.7%, P = 0.73), or involvement of cecum or terminal ileum (14.28% vs. 19.63%, P = 1). Postoperative treatment with antibiotics was more prevalent during COVID-19 (37.1% vs. 18%, P = 0.04). Length of stay (1.82 ± 2.04 vs. 2.74 ± 4.68, P = 0.2) and readmission rates (6% vs. 11.3%, P =0.51) were similar. CONCLUSIONS: The COVID-19 pandemic did not significantly affect the presentation, clinical course, management, and outcomes of patients presenting with acute appendicitis.

The Effect of the COVID-19 Outbreak on Trauma-Related Visits to a Tertiary Hospital Emergency Department.

BACKGROUND: The novel coronavirus disease (COVID-19) pandemic changed medical environments worldwide. OBJECTIVES: To evaluate the impact of the COVID-19 pandemic on trauma-related visits to the emergency department (ED). METHODS: A single tertiary center retrospective study was conducted that compared ED attendance of patients with injury-related morbidity between March 2020 (COVID-19 outbreak) and pre-COVID-19 periods: February 2020 and the same 2 months in 2018 and 2019. RESULTS: Overall, 6513 patients were included in the study. During the COVID-19 outbreak, the daily number of patients visiting the ED for acute trauma declined by 40% compared to the average in previous months (P < 0.01). A strong negative correlation was found between the number of trauma-related ED visits and the log number of confirmed cases of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in Israel (Pearson's r = -0.63, P < 0.01). In the COVID-19 period there was a significant change in the proportion of elderly patients (7% increase, P = 0.002), admissions ratio (12% increase, P < 0.001), and patients brought by emergency medical services (10% increase, P < 0.001). The number of motor vehicle accident related injury declined by 45% (P < 0.01). CONCLUSIONS: A significant reduction in the number of trauma patients presenting to the ED occurred during the COVID-19 pandemic, yet trauma-related admissions were on the rise.

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.

BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications. OBJECTIVE: Evaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED. DESIGN: An institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18-100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012-December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2-30 days post ED registration). A gradient boosting model was trained on data from years 2012-2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality. KEY RESULTS: Overall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality. CONCLUSION: The gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED.

Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Deep learning is an innovative algorithm based on neural networks. Wireless capsule endoscopy (WCE) is considered the criterion standard for detecting small-bowel diseases. Manual examination of WCE is time-consuming and can benefit from automatic detection using artificial intelligence (AI). We aimed to perform a systematic review of the current literature pertaining to deep learning implementation in WCE. METHODS: We conducted a search in PubMed for all original publications on the subject of deep learning applications in WCE published between January 1, 2016 and December 15, 2019. Evaluation of the risk of bias was performed using tailored Quality Assessment of Diagnostic Accuracy Studies-2. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. RESULTS: Of the 45 studies retrieved, 19 studies were included. All studies were retrospective. Deep learning applications for WCE included detection of ulcers, polyps, celiac disease, bleeding, and hookworm. Detection accuracy was above 90% for most studies and diseases. Pooled sensitivity and specificity for ulcer detection were .95 (95% confidence interval [CI], .89-.98) and .94 (95% CI, .90-.96), respectively. Pooled sensitivity and specificity for bleeding or bleeding source were .98 (95% CI, .96-.99) and .99 (95% CI, .97-.99), respectively. CONCLUSIONS: Deep learning has achieved excellent performance for the detection of a range of diseases in WCE. Notwithstanding, current research is based on retrospective studies with a high risk of bias. Thus, future prospective, multicenter studies are necessary for this technology to be implemented in the clinical use of WCE.

Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy.

BACKGROUND AND AIMS: The aim of our study was to develop and evaluate a deep learning algorithm for the automated detection of small-bowel ulcers in Crohn's disease (CD) on capsule endoscopy (CE) images of individual patients. METHODS: We retrospectively collected CE images of known CD patients and control subjects. Each image was labeled by an expert gastroenterologist as either normal mucosa or containing mucosal ulcers. A convolutional neural network was trained to classify images into either normal mucosa or mucosal ulcers. First, we trained the network on 5-fold randomly split images (each fold with 80% training images and 20% images testing). We then conducted 10 experiments in which images from n - 1 patients were used to train a network and images from a different individual patient were used to test the network. Results of the networks were compared for randomly split images and for individual patients. Area under the curves (AUCs) and accuracies were computed for each individual network. RESULTS: Overall, our dataset included 17,640 CE images from 49 patients: 7391 images with mucosal ulcers and 10,249 images of normal mucosa. For randomly split images results were excellent, with AUCs of .99 and accuracies ranging from 95.4% to 96.7%. For individual patient-level experiments, the AUCs were also excellent (.94-.99). CONCLUSIONS: Deep learning technology provides accurate and fast automated detection of mucosal ulcers on CE images. Individual patient-level analysis provided high and consistent diagnostic accuracy with shortened reading time; in the future, deep learning algorithms may augment and facilitate CE reading.

Comparison of deep learning models for natural language processing-based classification of non-English head CT reports.

PURPOSE: Natural language processing (NLP) can be used for automatic flagging of radiology reports. We assessed deep learning models for classifying non-English head CT reports. METHODS: We retrospectively collected head CT reports (2011-2018). Reports were signed in Hebrew. Emergency department (ED) reports of adult patients from January to February for each year (2013-2018) were manually labeled. All other reports were used to pre-train an embedding layer. We explored two use cases: (1) general labeling use case, in which reports were labeled as normal vs. pathological; (2) specific labeling use case, in which reports were labeled as with and without intra-cranial hemorrhage. We tested long short-term memory (LSTM) and LSTM-attention (LSTM-ATN) networks for classifying reports. We also evaluated the improvement of adding Word2Vec word embedding. Deep learning models were compared with a bag-of-words (BOW) model. RESULTS: We retrieved 176,988 head CT reports for pre-training. We manually labeled 7784 reports as normal (46.3%) or pathological (53.7%), and 7.1% with intra-cranial hemorrhage. For the general labeling, LSTM-ATN-Word2Vec showed the best results (AUC = 0.967 ± 0.006, accuracy 90.8% ± 0.01). For the specific labeling, all methods showed similar accuracies between 95.0 and 95.9%. Both LSTM-ATN-Word2Vec and BOW had the highest AUC (0.970). CONCLUSION: For a general use case, word embedding using a large cohort of non-English head CT reports and ATN improves NLP performance. For a more specific task, BOW and deep learning showed similar results. Models should be explored and tailored to the NLP task.