Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review.

OBJECTIVES: With smartphones and wearable devices becoming ubiquitous, they offer an opportunity for large-scale voice sampling. This systematic review explores the application of deep learning models for the automated analysis of voice samples to detect vocal cord pathologies. METHODS: We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. We searched MEDLINE and Embase databases for original publications on deep learning applications for diagnosing vocal cord pathologies between 2002 and 2022. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). RESULTS: Out of the 14 studies that met the inclusion criteria, data from a total of 3037 patients were analyzed. All studies were retrospective. Deep learning applications targeted Reinke's edema, nodules, polyps, cysts, unilateral cord paralysis, and vocal fold cancer detection. Most pathologies had detection accuracy above 90%. Thirteen studies (93%) exhibited a high risk of bias and concerns about applicability. CONCLUSIONS: Technology holds promise for enhancing the screening and diagnosis of vocal cord pathologies. While current research is limited, the presented studies offer proof of concept for developing larger-scale solutions.

Elevated International Normalized Ratio and Mortality in Hospitalized Patients Treated with Direct Oral Anticoagulants.

BACKGROUND: Direct oral anticoagulants (DOACs) are associated with a prolongation of the prothrombin time and an increased international normalized ratio (INR). The clinical significance of these changes is unclear. This study aimed to examine the association between an elevated INR on admission and in-hospital death and long-term survival in patients treated with DOACs. METHODS: Data were retrospectively retrieved from records of hospitalized patients at the Sheba Medical Center between November 2008 and July 2023. Patients were selected based on DOAC treatment, coagulation profile, and INR test done within 48 hours of hospitalization. The outcomes were in-hospital mortality and mortality in the year following hospitalization. RESULTS: The study included 11,399 hospitalized patients treated with DOACs. Patients with elevated INR had a 180% higher risk of in-hospital mortality (adjusted odds ratio 2.80; 95% confidence interval, 2.30-3.39) and a 57% increased risk of death during the following year (adjusted hazard ratio 1.57; 95% confidence interval, 1.44-1.71). Similar results were observed in subgroup analyses for each DOAC. CONCLUSIONS: An elevated INR on admission is associated with a higher risk for in-hospital death and increased risk for mortality during the first year following hospitalization in hospitalized patients treated with DOACs. This highlights that elevated INR levels in patients on DOACs should not be dismissed as laboratory variations due to DOAC treatment, as they may serve as a prognostic marker.

The Effect of The Oral Iron Chelator Deferiprone on Iron Overload and Oxidative Stress in Patients with Myelodysplastic Syndromes A Study by the Israeli MDS Working Group.

BACKGROUND: Most patients with lower risk myelodysplastic neoplasms (MDS) become RBC transfusion-dependent, resulting in iron overload, which is associated with an increased oxidative stress state. Iron-chelation therapy is applied to attenuate the toxic effects of this state. Deferiprone (DFP) is an oral iron chelator, which is not commonly used in this patient population, due to safety concerns, mainly agranulocytosis. The purpose of this study was to assess the effect of DFP, on oxidative stress parameters in iron overloaded RBC transfusion-dependent patients with lower risk MDS. METHODS: Adult lower-risk MDS patients with a cumulative transfusion burden of >20 red blood cells units and evidence of iron overload (serum ferritin >1,000 ng/mL) were included in this study. DFP was administered (100 mg/kg/day) for 4 months. Blood samples for oxidative stress parameters and iron overload parameters were done at baseline and monthly: reactive oxygen species (ROS), phosphatidylserine, reduced glutathione, membrane lipid peroxidation, serum ferritin and cellular labile iron pool. The primary efficacy variable was ROS. Tolerability and side-effects were recorded as well. A paired t-test was applied for statistical analyses. RESULTS: Eighteen patients were treated with DFP. ROS significantly decreased in all cell lineages: median decrease of 58.6% in RBC, 33.3% in PMN, and 39.8% in platelets (p<0.01 for all). Other oxidative stress markers improved: phosphatidylserine decreased by 57.95%, lipid peroxidase decreased by 141.3%, and reduced gluthathione increased by 72.8% (p<0.01 for all). The iron-overload marker, cellular labile iron pool, decreased by 35% in RBCs, 44.3% in PMN, and 46.3% in platelets (p<0.01 for all). No significant changes were observed in SF levels. There were no events of agranulocytosis. All AEs were grade 1-2. CONCLUSIONS: Herein we showed preliminary evidence that DFP decreases iron-induced oxidative stress in MDS patients with a good tolerability profile (albeit a short follow-up period). No cases of severe neutropenia or agranulocytosis were reported. The future challenge is to prove that reduction in iron toxicity will eventually be translated into a clinically meaningful improvement.

Adversarial attacks in radiology - A systematic review.

PURPOSE: The growing application of deep learning in radiology has raised concerns about cybersecurity, particularly in relation to adversarial attacks. This study aims to systematically review the literature on adversarial attacks in radiology. METHODS: We searched for studies on adversarial attacks in radiology published up to April 2023, using MEDLINE and Google Scholar databases. RESULTS: A total of 22 studies published between March 2018 and April 2023 were included, primarily focused on image classification algorithms. Fourteen studies evaluated white-box attacks, three assessed black-box attacks and five investigated both. Eleven of the 22 studies targeted chest X-ray classification algorithms, while others involved chest CT (6/22), brain MRI (4/22), mammography (2/22), abdominal CT (1/22), hepatic US (1/22), and thyroid US (1/22). Some attacks proved highly effective, reducing the AUC of algorithm performance to 0 and achieving success rates up to 100 %. CONCLUSIONS: Adversarial attacks are a growing concern. Although currently the threats are more theoretical than practical, they still represent a potential risk. It is important to be alert to such attacks, reinforce cybersecurity measures, and influence the formulation of ethical and legal guidelines. This will ensure the safe use of deep learning technology in medicine.

Current Applications of Artificial Intelligence in Sarcoidosis.

PURPOSE: Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis. METHODS: Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded. CONCLUSIONS: Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.

MRI features of intraductal papillary mucinous neoplasm of the bile ducts, "The myth about the cyst": A systematic review.

Rationale and objectives: Intraductal papillary mucinous neoplasm of the bile ducts (IPMN-B) is a true pre-cancerous lesion, which shares common features with pancreatic IPMN (IPMN-P). While IPMN-P is a well described entity for which guidelines were formulated and revised, IPMN-B is a poorly described entity.We carried out a systematic review to evaluate the existing literature, emphasizing the role of MRI in IPMN-B depiction. Materials and methods: PubMed database was used to identify original studies and case series that reported MR Imaging features of IPMN-B. The search keywords were "IPMN OR intraductal papillary mucinous neoplasm OR IPNB OR intraductal papillary neoplasm of the bile duct AND Biliary OR biliary cancer OR hepatic cystic lesions". Risk of bias and applicability were evaluated using the QUADAS-2 tool. Results: 884 Records were Identified through database searching. 12 studies satisfied the inclusion criteria, resulting in MR features of 288 patients. All the studies were retrospective. Classic features of IPMN-B are under-described. Few studies note worrisome features, concerning for an underlying malignancy. 50 % of the studies had a high risk of bias and concerns regarding applicability. Conclusions: The MRI features of IPMN-B are not well elaborated and need to be further studied. Worrisome features and guidelines regarding reporting the imaging findings should be established and published. Radiologists should be aware of IPMN-B, since malignancy diagnosis in an early stage will yield improved prognosis.

Fluctuations in Serum Creatinine Levels During Hospitalization and Long-Term End-Stage Kidney Disease and Mortality.

Importance: Acute kidney injury is associated with poor outcomes, but the clinical implication of reversible serum creatinine level fluctuations during hospitalization not necessarily defined as acute kidney injury is poorly understood. Objective: To investigate the long-term outcomes of patients without previously diagnosed kidney disease who present with decreased kidney function and are subsequently discharged with apparently normal kidney function. Design, Setting, and Participants: A retrospective cohort study was conducted of patients hospitalized in a large tertiary hospital in Israel between September 1, 2007, and July 31, 2022. The study included patients admitted to an internal medicine ward. Patients had not undergone dialysis during the index hospitalization, had at least 3 creatinine tests performed during hospitalization, and had a discharge estimated glomerular filtration rate (eGFR) exceeding 60 mL/min/1.73 m2. Patients with preexisting chronic kidney disease were excluded. Exposure: Glomerular filtration rate was estimated from serum creatinine values using the updated 2022 Chronic Kidney Disease Epidemiology Collaboration formula, and eGFR greater than 60 mL/min/1.73 m2 was regarded as normal. Exposure was defined based on the association between the first and last values determined during hospitalization. Main Outcomes and Measures: All-cause mortality in the year following the index hospitalization and end-stage kidney disease (ESKD) in the 10 years following the index hospitalization. Results: A total of 40 558 patients were included. Median age was 69 (IQR, 56-80) years, with 18 004 women (44%) and 22 554 men (56%). A total of 34 332 patients (85%) were admitted with a normal eGFR and 6226 (15%) with decreased eGFR. Patients with decreased eGFR on presentation had an 18% increased mortality in the year following hospitalization (adjusted hazard ratio [AHR], 1.18; 95% CI, 1.11-1.24) and a 267% increased risk of ESKD in the 10 years following hospitalization (AHR, 3.67; 95% CI, 2.43-5.54), despite having been discharged with apparently normal kidney function. The highest risk was noted in patients who presented to the hospital with an eGFR of 0 to 45 mL/min/1.73 m2. Conclusions and Relevance: The findings of this cohort study suggest that patients who present with decreased kidney function and are discharged without clinically evident residual kidney disease may be at increased long-term risk for ESKD and mortality.

Jejunal Inflammation in Crohn's Disease: Comparison between Diffusion Weighted Magnetic Resonance Imaging and Video Capsule Endoscopy.

BACKGROUND: Jejunal disease is associated with worse prognosis in Crohn's disease. The added value of diffusion weighted imaging for evaluating jejunal inflammation related to Crohn's Disease is scarce. OBJECTIVES: To compare diffusion weighted imaging, video capsule endoscopy, and inflammatory biomarkers in the assessment of Crohn's disease involving the jejunum. METHODS: Crohn's disease patients in clinical remission were prospectively recruited and underwent magnetic resonance (MR)-enterography and video capsule endoscopy. C-reactive protein and fecal-calprotectin levels were obtained. MR-enterography images were evaluated for restricted diffusion, and apparent diffusion coefficient values were measured. The video capsule endoscopy-based Lewis score was calculated. Associations between diffusion weighted imaging, apparent diffusion coefficient, Lewis score, and inflammatory biomarkers were evaluated. RESULTS: The study included 51 patients, and 27/51 (52.9%) with video capsule endoscopies showed jejunal mucosal inflammation. Sensitivity and specificity of restricted diffusion for video capsule endoscopy mucosal inflammation were 59.3% and 37.5% for the first reader, and 66.7% and 37.5% for the second reader, respectively. Diffusion weighted imaging was not statistically associated with jejunal video capsule endoscopy inflammation (P = 0.813). CONCLUSIONS: Diffusion weighted imaging was not an effective test for evaluation of jejunal inflammation as seen by video capsule endoscopy in patients with quiescent Crohn's disease.

Spatiotemporal analysis of small bowel capsule endoscopy videos for outcomes prediction in Crohn's disease.

Background: Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn's disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined. Objectives: We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients. Design: This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software. Methods: CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis. Results: The patient cohort included 101 patients. The median duration of follow-up was 902 (354-1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74. Conclusion: Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD.

Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence.

Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn's disease (CD). In 2017, the panenteric capsule (PillCam Crohn's system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.

Self-reported treatment effectiveness for Crohn's disease using a novel crowdsourcing web-based platform.

BACKGROUND AND AIMS: Internet and social media platforms have become an unprecedented source for sharing self-experience, potentially allowing the collection and integration of health data with patient experience. StuffThatWorks (STW) is an online open platform that applies machine learning and the power of crowdsourcing, where patients with chronic medical conditions can self-report and compare their individual outcomes using a structured online questionnaire. We aimed to conduct a cross-sectional, international, crowdsourcing, artificial-intelligence (AI) web-based study of patients with Crohn's disease (CD) self-reporting their outcomes. METHODS: A proprietary STW Bayesian inference model was built to measure improvement in CD severity (on scale of 1-5) for each treatment and ranked treatments using effectiveness. The effectiveness of first-line biological treatments was analyzed by multiple comparisons and by calculating odds ratios and 95% confidence intervals for each treatment pair. RESULTS: We included 7593 self-reported CD patients for the analysis. Most of the participants were female (75.8%) and from English-speaking countries (95.7%). Overall, anti-TNF drugs were the most reported tried treatment (52.8%). Infliximab (IFX) was ranked as the most effective treatment by the STW effectiveness model followed by bowel surgery (second), adalimumab (ADA, third), ustekinumab (UST, 4rd), and vedolizumab (VDZ, fifth). In paired comparison analyses, IFX was most effective, ADA had similar effectiveness compared to UST and all three were more effective than VDZ. CONCLUSION: We present the first online crowdsourcing AI platform-based study of self-reported treatment effectiveness in CD. Net-based crowdsourcing patient-reported outcome platforms can potentially help both clinicians and patients select the best treatment for their condition.

Anticoagulant treatment for pediatric splanchnic vein thrombosis: a systematic review and meta-analysis.

BACKGROUND: The clinical characteristics of splanchnic vein thrombosis (SVT) in pediatric patients and its optimal treatment strategies are unknown. OBJECTIVES: This study aimed to assess the effectiveness and safety of anticoagulant therapy for pediatric SVT. METHODS: MEDLINE and EMBASE databases were searched up to December 2021. We included observational and interventional studies that enrolled pediatric patients with SVT and reported anticoagulant treatment and outcomes, including rates of vessel recanalization, SVT extension, venous thromboembolism (VTE) recurrence, major bleeding, and mortality. Pooled proportions of vessel recanalization were calculated with their 95% CI. RESULTS: A total of 506 pediatric patients (aged 0-18 years) across 17 observational studies were included. The majority of patients had portal vein thrombosis (n = 308, 60.8%) or Budd-Chiari syndrome (n = 175, 34.6%). Most events were triggered by transient provoking factors. Anticoagulation (heparins and vitamin K antagonists) was prescribed in 217 (42.9%) patients, and 148 (29.2%) patients underwent vascular interventions. The overall pooled proportions of vessel recanalization were 55.3% (95% CI, 34.1%-74.7%; I2 = 74.0%) among anticoagulated patients and 29.4% (95% CI, 2.6%-86.6%; I2 = 49.0%) among non-anticoagulated patients. SVT extension, major bleeding, VTE recurrence, and mortality rates were 8.9%, 3.8%, 3.5%, and 10.0%, respectively, in anticoagulated patients and 2.8%, 1.4%, 0%, and 50.3%, respectively, in non-anticoagulated patients. CONCLUSION: In pediatric SVT, anticoagulation appears to be associated with moderate recanalization rates and a low risk of major bleeding. VTE recurrence is low and comparable to that reported in pediatric patients with other types of provoked VTE.

Multimodal fusion models for pulmonary embolism mortality prediction.

Pulmonary embolism (PE) is a common, life threatening cardiovascular emergency. Risk stratification is one of the core principles of acute PE management and determines the choice of diagnostic and therapeutic strategies. In routine clinical practice, clinicians rely on the patient's electronic health record (EHR) to provide a context for their medical imaging interpretation. Most deep learning models for radiology applications only consider pixel-value information without the clinical context. Only a few integrate both clinical and imaging data. In this work, we develop and compare multimodal fusion models that can utilize multimodal data by combining both volumetric pixel data and clinical patient data for automatic risk stratification of PE. Our best performing model is an intermediate fusion model that incorporates both bilinear attention and TabNet, and can be trained in an end-to-end manner. The results show that multimodality boosts performance by up to 14% with an area under the curve (AUC) of 0.96 for assessing PE severity, with a sensitivity of 90% and specificity of 94%, thus pointing to the value of using multimodal data to automatically assess PE severity.

Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy.

OBJECTIVE: This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring. DATA SOURCES: A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms. STUDY ELIGIBILITY CRITERIA: Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916). METHODS: Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist. RESULTS: Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias. CONCLUSION: The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.

Diffusion-weighted MRI for predicting and assessing treatment response of liver metastases from CRC - A systematic review and meta-analysis.

The evaluation of response to chemotherapy and targeted therapies in colorectal liver metastases has traditionally been based on size changes, as per the RECIST criteria. However, therapy may alter tissue composition and not only tumor size, therefore, functional imaging techniques such as diffusion-weighted magnetic resonance imaging (DWI) may offer a more comprehensive assessment of treatment response. The aim of this systematic review and meta-analysis was to evaluate the use of DWI in the prediction and assessment of response to treatment in colorectal liver metastases and to determine if there is a baseline apparent diffusion coefficient (ADC) cut-off value that can predict a favorable response. A literature search was conducted using the MEDLINE/PubMed database, and risk of bias was evaluated using the QUADAS-2 tool. The mean differences between responders and non-responders were pooled. A total of 16 studies met the inclusion criteria, and various diffusion-derived techniques and coefficients were found to have potential for predicting and assessing treatment response. However, discrepancies were noted between studies. The most consistent predictor of response was a lower baseline ADC value calculated using traditional mono-exponential methods. Non-mono-exponential techniques for calculating DWI-derived parameters were also reported. A meta-analysis of a subset of studies failed to establish a cut-off value of ADC due to heterogeneity, but revealed a pooled mean difference of -0.12 × 10-3 mm2/s between responders and non-responders. The results of this systematic review suggest that diffusion-derived techniques and coefficients may contribute to the evaluation and prediction of treatment response in colorectal liver metastases. Further controlled prospective studies are needed to confirm these findings and to guide clinical and radiological decision-making in the management of patients with CRC liver metastases.

A systematic review of adeno-associated virus gene therapy clinical trials for HIV - A potential solution for patients with haemophilia and HIV?

INTRODUCTION: In the past HIV infection was a common complication of haemophilia therapy. Gene therapy trials in Haemophilia patients using rAAV have shown promising results; Unfortunately, the majority of gene therapy trials studies have excluded HIV positive patients. We decided to systematically review the published clinical trials using rAAV for HIV prevention. METHODS: A comprehensive literature search was performed to identify studies evaluating clinical trials using rAAV for HIV. The search was conducted using the MEDLINE/PubMed databases. Search keywords included 'gene therapy', 'adeno-associated virus', 'HIV' and 'clinical trial'. RESULTS: Three studies met our inclusion criteria. Two were phase 1 studies and one was a phase 2 study. One study examined an AAV coding for human monoclonal IgG1 antibody whereas the other two studies delivered a vector coding for viral protease and part of reverse transcriptase. All studies administered the vaccine intramuscularly and showed a response as well a good safety profile. DISCUSSION: The concept of using a viral vector to prevent a viral infection is revolutionary. Due to the paucity of information regarding application of any gene therapy in HIV patients and the potential use of gene therapy in haemophilia patients with HIV in the future warrants attention.

Stroke mortality prediction using machine learning: systematic review.

BACKGROUND AND AIMS: Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning-based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality. MATERIALS AND METHODS: We searched MEDLINE/PubMed and Web of Science databases for original publications on machine learning applications in stroke mortality prediction, published between January 1, 2011, and October 27, 2022. Risk of bias and applicability were evaluated using the tailored QUADAS-2 tool. RESULTS: Of the 1015 studies retrieved, 28 studies were included. Twenty-Five studies were retrospective. The ML models demonstrated a favorable range of AUC for mortality prediction (0.67-0.98). In most of the articles, the models were applied for short-term post stroke mortality. The number of explanatory features used in the models to predict mortality ranged from 5 to 200, with substantial overlap in the variables included. Age, high BMI and high NIHSS score were identified as important predictors for mortality. Almost all studies had a high risk of bias in at least one category and concerns regarding applicability. CONCLUSION: Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. Thus, future prospective, multicenter studies with standardized reports are crucial to firmly establish the usefulness of the algorithms in stroke prognostication.

Mortality in Patients Who Underwent Computed Tomography Angiography for a Suspected Acute Mesenteric Ischemia as a Final Alternative Diagnosis.

BACKGROUND: Acute mesenteric ischemia (AMI) is a medical condition with high levels of morbidity and mortality. However, most patients suspected of AMI will eventually have a different diagnosis. Nevertheless, these patients have a high risk for co-morbidities. OBJECTIVES: To analyze patients with suspected AMI with an alternative final diagnosis, and to evaluate a machine learning algorithm for prognosis prediction in this population. METHODS: In a retrospective search, we retrieved patient charts of those who underwent computed tomography angiography (CTA) for suspected AMI between January 2012 and December 2015. Non-AMI patients were defined as patients with negative CTA and a final clinical diagnosis other than AMI. Correlation of past medical history, laboratory values, and mortality rates were evaluated. We evaluated gradient boosting (XGBoost) model for mortality prediction. RESULTS: The non-AMI group comprised 325 patients. The two most common groups of diseases included gastrointestinal (33%) and biliary-pancreatic diseases (27%). Mortality rate was 24.6% for the entire cohort. Medical history of chronic kidney disease (CKD) had higher risk for mortality (odds ratio 2.2). Laboratory studies revealed that lactate dehydrogenase (LDH) had the highest diagnostic ability for predicting mortality in the entire cohort (AUC 0.70). The gradient boosting model showed an area under the curve of 0.82 for predicting mortality. CONCLUSIONS: Patients with suspected AMI with an alternative final diagnosis showed a 25% mortality rate. A past medical history of CKD and elevated LDH were associated with increased mortality. Non-linear machine learning algorithms can augment single variable inputs for predicting mortality.