Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.

Background and Objectives: The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent developments of artificial intelligence (AI) applications show a high potential for improving the accuracy of diagnosis, involving large sets of clinical data. For this reason, the aim of this systematic review is to provide a comprehensive overview of current AI applications and analyze the accuracy of these systems to perform an automated diagnosis of liver fibrosis. Materials and Methods: We searched PubMed, Cochrane Library, EMBASE, and WILEY databases using predefined keywords. Articles were screened for relevant publications about AI applications capable of diagnosing liver fibrosis. Exclusion criteria were animal studies, case reports, abstracts, letters to the editor, conference presentations, pediatric studies, studies written in languages other than English, and editorials. Results: Our search identified a total of 24 articles analyzing the automated imagistic diagnosis of liver fibrosis, out of which six studies analyze liver ultrasound images, seven studies analyze computer tomography images, five studies analyze magnetic resonance images, and six studies analyze liver biopsies. The studies included in our systematic review showed that AI-assisted non-invasive techniques performed as accurately as human experts in detecting and staging liver fibrosis. Nevertheless, the findings of these studies need to be confirmed through clinical trials to be implemented into clinical practice. Conclusions: The current systematic review provides a comprehensive analysis of the performance of AI systems in diagnosing liver fibrosis. Automatic diagnosis, staging, and risk stratification for liver fibrosis is currently possible considering the accuracy of the AI systems, which can overcome the limitations of non-invasive diagnosis methods.

Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning.

The goal of this paper is to provide a Machine Learning-based solution that can be utilized to automate the Chicago Classification algorithm, the state-of-the-art scheme for esophageal motility disease identification. First, the photos were preprocessed by locating the area of interest-the precise instant of swallowing. After resizing and rescaling the photos, they were utilized as input for the Deep Learning models. The InceptionV3 Deep Learning model was used to identify the precise class of the IRP. We used the DenseNet201 CNN architecture to classify the images into 5 different classes of swallowing disorders. Finally, we combined the results of the two trained ML models to automate the Chicago Classification algorithm. With this solution we obtained a top-1 accuracy and f1-score of 86% with no human intervention, automating the whole flow, from image preprocessing until Chicago classification and diagnosis.

Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning.

High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest-the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.

The Efficacy of Hypnotherapy in the Treatment of Functional Dyspepsia.

BACKGROUND: Functional dyspepsia (FD) is one of the most frequent functional gastrointestinal disorders and is defined using the Rome IV criteria as any combination of the following symptoms: postprandial fullness, early satiety, epigastric pain, and epigastric burning that are severe enough to interfere with the usual activities and occur at least 3 days per week over the past 3 months with an onset of at least 6 months before the presentation. The purpose of this systematic review is to analyze all the relevant studies in the literature that investigate the efficiency of hypnotherapy in FD. AREAS OF UNCERTAINTY: FD refractory to conservative treatment is a therapeutic challenge, and alternative treatment options are needed. Gut-oriented hypnotherapy has been reported an effective treatment for irritable bowel syndrome, but poorly tested in FD. DATA SOURCES: We performed a search in 6 bibliographic databases (PubMed, Embase, Cochrane Library, Web of Science, Scopus, and LILACS) using customized search strategies for each engine. The search strategy included the following terms: (hypnosis, hypnotherapy, hypnotherapies, hypnogenesis, hypnotism, hypnotist, hypnotical suggestion, suggestion, and mesmerism) and {[functional and (dyspepsia or dyspeptic)] or FD}. RESULTS: Taking the aforementioned criteria into account, the result was a review of 4 articles analyzing the efficacy of hypnotherapy in the treatment of FD, published in the past 20 years. The initial search identified 398 articles, of which 37 potentially appropriate articles were reviewed. Of these 37 articles, 4 articles were included in the review. The benefits observed by numerous studies go beyond the field of digestive pathology, patients describing a general improvement in physical and mental health. CONCLUSIONS: Current studies analyzing the efficacy of hypnotherapy in FD provide encouraging data, but additional randomized controlled trials are needed before a firm position on the effectiveness of hypnosis in FD.

Coping Strategies and Inflammatory Bowel Disease: A Narrative Review.

Background: Coping strategies play a crucial role in managing inflammatory bowel disease (IBD), influencing both health-related quality of life (HRQoL) and psychological well-being. This study systematically reviews the available literature to analyze coping mechanisms in IBD populations and their impact. Methods: Relevant English-language studies published until 2023 were identified through a comprehensive search of PubMed, EMBASE, EBSCOhost, and Cochrane Library. After applying inclusion and exclusion criteria, 57 articles underwent full analysis. Results: The findings highlight the diversity of coping strategies used by individuals with IBD and emphasize the need for a nuanced approach considering factors like disease severity, duration, and individual characteristics. This review underlines the influence of coping mechanisms on QoL and indicates their potential to aid IBD management and rehabilitation. Conclusions: This study underscores the value of investigating coping strategies to promote better outcomes for individuals with IBD. Future research should explore personalized interventions that address the heterogeneity of the IBD population.

The Efficiency of Serum Biomarkers in Predicting the Clinical Outcome of Patients with Mesenteric Ischemia during Follow-Up: A Systematic Review.

The initial clinical manifestation of acute mesenteric ischemia poses a diagnostic challenge, often leading to delays in identification and subsequent surgical intervention, contributing to adverse outcomes. Serum biomarkers, offering insights into the underlying pathophysiology, hold promise as prognostic indicators for acute mesenteric ischemia. This systematic review comprehensively explores the role of blood biomarkers in predicting clinical outcomes during follow-up for patients with mesenteric ischemia. A thorough literature search across the PubMed, Cochrane Library, and EMBASE databases yielded 33 relevant publications investigating the efficacy of serum biomarkers in predicting outcomes for mesenteric ischemia. Numerous studies underscore the utility of blood biomarkers in swiftly and accurately differentiating between causes of mesenteric ischemia, facilitating a prompt diagnosis. Elevated levels of specific biomarkers, particularly D-dimers, consistently correlate with heightened mortality risk and poorer clinical outcomes. While certain serum indicators exhibit substantial potential in associating with mesenteric ischemia, further research through rigorous human trials is imperative to enhance their consistent predictive ability during the follow-up period. This study underscores the diagnostic and prognostic significance of specific biomarkers for mesenteric ischemia, emphasizing the necessity for standardized procedures in future investigations.

New diagnostic and therapeutic procedures applied in pediatric esophageal achalasia in a pediatric tertiary center: A case series.

Achalasia is an esophageal motor disorder that is rare in children. While the condition is uncommon, it is especially difficult to diagnose in pediatric patients; however, the ability to form a diagnosis has progressed markedly with the advent of esophageal high-resolution manometry (HRM). The aim of the present study was to highlight particularities of the diagnosis, based on esophageal HRM, as well as the treatment of achalasia in children. The current study analyzed cases of achalasia from a single pediatric tertiary center, Clinical Emergency Hospital for Children (Cluj-Napoca, Romania). The clinical data and the results of the investigations of seven children with achalasia, the first children to be evaluated using esophageal HRM in the center, were reported. The patients were aged between 11 and 18 years. All the patients were newly diagnosed with achalasia, except for one. The duration of symptoms was between 4 months and 2 years in the newly diagnosed patients. All the patients were assessed with conventional esophageal manometry and/or esophageal HRM. A multidisciplinary team contributed to the diagnosis and the management of achalasia. A total of 4 children diagnosed with type II achalasia were treated with peroral endoscopic myotomy (POEM) and 3 of the patients were treated with pneumatic dilations. Overall, achalasia is a rare but challenging condition in children. A diagnosis starts with a clinical suspicion based on swallowing disorders and upper digestive endoscopy, and is confirmed by esophageal HRM. Therapy should be adapted to the type of achalasia, the age of the children and the severity. In the present study, a relatively recent treatment option, POEM, was applied in pediatric patients with minor immediate adverse events. The report of these cases adds to the limited experience of using HRM and POEM in children with achalasia.

Supragastric belching: Pathogenesis, diagnostic issues and treatment.

Belching is defined as an audible escape of air from the esophagus or the stomach into the pharynx. It becomes pathologic if it is excessive and becomes bothersome. According to Rome IV diagnostic criteria, there is a belching disorder when one experiences bothersome belching (severe enough to impact on usual activities) more than 3 days a week. Esophageal impedance can differentiate between gastric and supragastric belching. The aim of this review was to provide data on pathogenesis and diagnosis of supragastric belching and study its relationship with gastroesophageal reflux disease and psychological factors. Treatment options for supragastric belching are also presented.

Clinical and Metabolomic Effects of Lactiplantibacillus plantarum and Pediococcus acidilactici in Fructose Intolerant Patients

Fructose intolerance (FI) is a widespread non-genetic condition in which the incomplete absorption of fructose leads to gastro-intestinal disorders. The crucial role of microbial dysbiosis on the onset of these intolerance symptoms together with their persistence under free fructose diets are driving the scientific community towards the use of probiotics as a novel therapeutic approach. In this study, we evaluated the prevalence of FI in a cohort composed of Romanian adults with Functional Grastrointestinal Disorders (FGIDs) and the effectiveness of treatment based on the probiotic formulation EQBIOTA® (Lactiplantibacillus plantarum CECT 7484 and 7485 and Pediococcus acidilactici CECT 7483). We evaluated the impact of a 30-day treatment both on FI subjects and healthy volunteers. The gastrointestinal symptoms and fecal volatile metabolome were evaluated. A statistically significant improvement of symptoms (i.e., bloating, and abdominal pain) was reported in FI patient after treatment. On the other hand, at the baseline, the content of volatile metabolites was heterogeneously distributed between the two study arms, whereas the treatment led differences to decrease. From our analysis, how some metabolomics compounds were correlated with the improvement and worsening of clinical symptoms clearly emerged. Preliminary observations suggested how the improvement of gastrointestinal symptoms could be induced by the increase of anti-inflammatory and protective substrates. A deeper investigation in a larger patient cohort subjected to a prolonged treatment would allow a more comprehensive evaluation of the probiotic treatment effects.

Nursing procedures for advanced dementia: Traditional techniques versus autonomous robotic applications (Review).

The purpose of this study was to perform a comparative analysis of traditional nursing techniques and autonomous robotic applications used for managing patients with advanced stages of dementia. PubMed, Cochrane Library, EMBASE, and WILEY databases were searched for relevant articles concerning nursing techniques applied in the treatment of patients with advanced dementia. The search terms included: (advanced dementia OR severe dementia) AND [artificial intelligence (AI) OR robotic OR robots OR neural networks OR deep learning OR automated procedures OR autonomous application]. This search identified a total of 2,679 articles and 298 articles were selected. Finally, 23 articles were included in this systematic review, out of which 8 studies analyzed traditional nursing techniques and 15 studies analyzed autonomous robotic applications. Significant evidence was revealed, demonstrating that autonomous robotic applications used for patients with advanced stages of dementia are a feasible, cost-efficient solution and represent an excellent benefit for patients and the healthcare system.

Atherogenic Index of Plasma in Non-Alcoholic Fatty Liver Disease: Systematic Review and Meta-Analysis.

(1) Background: Approximately a billion people worldwide are affected by NAFLD, which places a high clinical burden and financial cost on society. Liver biopsy is the gold standard for diagnosing NAFLD, but its invasivity limits the early diagnosis of NAFLD. Hence, it is important to look for alternate techniques in detecting and diagnosing NAFLD. NAFLD is associated with atherosclerosis. The purpose of this study was to assess the effectiveness of the atherogenic index of plasma (AIP) as a non-invasive modality for predicting NAFLD. (2) Methods: A search using electronic databases PubMed, EMBASE, and Scopus was carried out to find observational studies, looking at research that had been published up until the date of 11 May 2022. The included studies' quality, risk of bias, and internal validity were evaluated using the QUADAS-2 quality assessment tool. The key summary outcomes were the mean difference (MD) and area under the curve (AUC). (3) Results: A total of eight studies (81,178 participants) were included in our review, while 17% of the included participants had NAFLD. A sex distribution of 57.8% men and 42.2% women was observed. The AIP between NAFLD and the controls was not significant (MD 0.212 [95% CI 0.231-0.655]). A significant MD in AIP between the males and females with NAFLD was observed (MD 0.246 [95% CI 0.098-0.395]). The AIP predicted NAFLD with an AUC of 0.764 as well as in males (AUC 0.761) and females (AUC 0.733). (4) Conclusions: There was a substantial MD in the AIP between both sexes, but there was no significant difference in the AIP values between patients with NAFLD and the controls. The AIP is a reliable biomarker for the diagnosis of NAFLD since its ability to predict the development of NAFLD was comparable to that of the other biomarkers.

Deep Learning and Antibiotic Resistance.

Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.

Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence.

BACKGROUND AND AIMS: High-resolution esophageal manometry (HREM) is the gold standard procedure used for the diagnosis of esophageal motility disorders (EMD). Artificial intelligence (AI) might provide an efficient solution for the automatic diagnosis of EMD by improving the subjective interpretation of HREM images. The aim of our study was to develop an AI-based system, using neural networks, for the automatic diagnosis of HREM images, based on one wet swallow raw image. METHODS: In the first phase of the study, the manometry recordings of our patients were retrospectively analyzed by three experienced gastroenterologists, to verify and confirm the correct diagnosis. In the second phase of the study raw images were used to train an artificial neural network. We selected only those tracings with ten test swallows that were available for analysis, including a total of 1570 images. We had 10 diagnosis categories, as follows: normal, type I achalasia, type II achalasia, type III achalasia, esophago-gastric junction outflow obstruction, jackhammer oesophagus, absent contractility, distal esophageal spasm, ineffective esophageal motility, and fragmented peristalsis, based on Chicago classification v3.0 for EMDs. RESULTS: The raw images were cropped, binarized, and automatically divided in 3 parts: training, testing, validation. We used Inception V3 CNN model, pre-trained on ImageNet. We developed a custom classification layer, that allowed the CNN to classify each wet swallow image from the HREM system into one of the diagnosis categories mentioned above. Our algorithm was highly accurate, with an overall precision of more than 93%. CONCLUSION: Our neural network approach using HREM images resulted in a high accuracy automatic diagnosis of EMDs.

Romanian Guidelines for the Diagnosis and Treatment of GERD-induced Respiratory Manifestations.

BACKGROUND AND AIMS: Gastroesophageal reflux disease (GERD) is a common condition present in daily practice with a wide range of clinical phenotypes. In this line, respiratory conditions may be associated with GERD. The Romanian Societies of Gastroenterology and Neurogastroenterology, in association with the Romanian Society of Pneumology, aimed to create a guideline regarding the epidemiology, diagnosis and treatment of respiratory conditions associated with GERD. METHODS: Delphi methodology was used and eleven common working groups of experts were created. The experts reviewed the literature according to GRADE criteria and formulated 34 statements and recommendations. Consensus (>80% agreement) was reached for some of the statements after all participants voted. RESULTS: All the statements and the literature review are presented in the paper, together with their correspondent grade of evidence and the voting results. Based on >80% voting agreement, a number of 22 recommendations were postulated regarding the diagnosis and treatment of GERD-induced respiratory symptoms. The experts considered that GERD may cause bronchial asthma and chronic cough in an important number of patients through micro-aspiration and vagal-mediated tracheobronchial reflex. GERD should be suspected in patients with asthma with suboptimal controlled or after exclusion of other causes, also in nocturnal refractory cough which needs gastroenterological investigations to confirm the diagnosis. Therapeutic test with double dose proton pump inhibitors (PPI) for 3 months is also useful. GERD induced respiratory conditions are difficult to treat; however,proton pump inhibitors and laparoscopic Nissen fundoplication are endorsed for therapy. CONCLUSIONS: This guideline could be useful for the multidisciplinary management of GERD with respiratory symptoms in current practice.

Digestive involvement in COVID-19: what we have learned in the past 12 months.

Background and aim: The year 2020 will remain in the medical history as the year of COVID-19 pandemic. Although COVID-19 is considered mainly a respiratory disease, it is well known now that SARS-CoV-2 can affect major organs including the digestive system. This is a comprehensive review on gastrointestinal involvement in the SARS-CoV-2 infection, also the digestive damage due to COVID-19. Methods: A literature search was undertaken in main databases of medical publications following the search items digestive or gastrointestinal or gastric or enteral and SARS-CoV-2 or COVID-19. Data on gastrointestinal symptoms and alterations in this viral condition were collected and presented. Only observational studies were included. Results: In this review we discuss the recent information on the relationship between COVID-19 and the digestive system. The putative underlying pathogenic mechanisms of COVID-19 as well as the clinical findings in relation to gastrointestinal changes are presented. Conclusions: SARS-CoV-2 virus can affect any part of the digestive system. Patients with chronic diseases are at greatest risk of adverse out-comes. There is a considerable risk for drug-drug interactions in patients who require a large treatment schedule.

Coping Strategies and Irritable Bowel Syndrome: A Systematic Review.

BACKGROUND AND AIMS: Irritable bowel syndrome (IBS) is associated with a high prevalence of psychiatric comorbidities. While psychosocial determinants were intently studied, coping strategies with stress used by IBS patients were never comprehensively reviewed. Therefore, this systematic review aimed to summarize the coping strategies used by IBS patients and to identify which tools are frequently used to measure coping strategies. METHODS: According to PRISMA guidelines, we searched for articles indexed in PubMed, EBSCOhost, EMBASE and Cochrane Library. The search terms included: (coping OR coping strategies OR coping mechanism) AND (irritable bowel syndrome OR IBS). The initial search identified 756 articles. After applying all filters (human filters, excluding conference abstracts and conference papers), 96 studies remained. Finally, a total of 21 articles were included in this systematic review. RESULTS: Twenty-one articles using fifteen coping instruments and six measures of quality of life were found. One was interventional, one longitudinal, and the rest were cross-sectional studies. One study was qualitative, while the rest used quantitative measures. Emotion-focused coping was associated with worse psychological outcomes, while the effect of problem-focused coping was not regularly associated with better psychological outcomes. Catastrophizing was negatively associated with health-related quality of life. Psychological distress (anxiety, depression) was significantly related to the impairment of health-related quality of life. CONCLUSION: Patients with IBS cope in different ways when confronted with health and daily-life stressors. The maladaptive strategy of coping is associated with poor health-related quality of life and psychiatric comorbidities but methodological problems limit conclusions regarding the strength and nature of this association. Future research needs to focus on which strategies are most effective at reducing psychological distress in IBS patients.

Helicobacter pylori infection and positive Rome IV criteria for functional dyspepsia in Romanian medical students.

Recent data suggest that the prevalence of Helicobacter pylori (HP) infection in Romania has been declining in the last 30 years. However, there are no studies regarding HP prevalence among medical students. The objectives of this study were to estimate the prevalence of HP infection and assess the prevalence of dyspepsia in medical students and the relationship between dyspepsia and infection. We included 150 students from the Iuliu Hatieganu University of Medicine and Pharmacy of Cluj-Napoca, Romania (102 females and 48 males, mean age 21 years). Each student completed a lifestyle questionnaire, personal history, family history as well as the Rome IV questionnaire for functional dyspepsia. The status of HP infection was determined using the C13-urea respiratory test. The prevalence of HP infection was 25.33%, and 18% met the Rome IV criteria for functional dyspepsia. 37% of students with functional dyspepsia had a positive HP test. Of all students, 8% had a history of HP infection. Those with a history of HP infection had a 4.45% (95% CI 1.6 - 12.37) higher risk of having positive Rome IV criteria for functional dyspepsia than those with no previous history of infection (p=0.008). Thus, the present study adds to the body of evidence regarding HP prevalence among medical students, 25.33% being positive. We found no statistically significant correlation between HP infection and functional dyspepsia. Those with a history of HP infection had a higher risk of functional dyspepsia.

Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a fast-growing pathology around the world, being considered the most common chronic liver disease. It is diagnosed based on the presence of steatosis in more than 5% of hepatocytes without significant alcohol consumption. This review aims to provide a comprehensive overview of current studies of artificial intelligence (AI) applications that may help physicians in implementing a complete automated NAFLD diagnosis and staging. METHODS: PubMed, EMBASE, Cochrane Library, and WILEY databases were screened for relevant publications in relation to AI applications in NAFLD. The search terms included: (non-alcoholic fatty liver disease OR NAFLD) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR computer-aided diagnosis OR digital pathology OR automated ultrasound OR automated computer tomography OR automated magnetic imaging OR electronic health records). RESULTS: Our search identified 37 articles about automated NAFLD diagnosis, out of which 15 articles analyzed imagistic techniques, 15 articles analyzed digital pathology, and 7 articles analyzed electronic health records (EHC). All studies included in this review show an accurate capacity of automated diagnosis and staging in NAFLD using AI-based software. CONCLUSIONS: We found significant evidence demonstrating that implementing a complete automated system for NAFLD diagnosis, staging, and risk stratification is currently possible, considering the accuracy, sensibility, and specificity of available AI-based tools.

Left Atrial Geometry and Phasic Function Determined by Cardiac Magnetic Resonance Are Independent Predictors for Outcome in Non-Ischaemic Dilated Cardiomyopathy.

Left atrial (LA) geometry and phasic functions are frequently impaired in non-ischaemic dilated cardiomyopathy (NIDCM). Cardiac magnetic resonance (CMR) can accurately measure LA function and geometry parameters. We sought to investigate their prognostic role in patients with NIDCM. We prospectively examined 212 patients with NIDCM (49 ± 14.2-year-old; 73.5% males) and 106 healthy controls. LA volumes, phasic functions, geometry, and fibrosis were determined using CMR. A composite outcome (cardiac death, ventricular tachyarrhythmias, heart failure hospitalization) was ascertained over a median of 26 months. LA phasic functions, sphericity index (LASI) and late gadolinium enhancement (LA-LGE) were considerably impaired in the diseased group (p < 0.001) and significantly correlated with impaired LV function parameters (p < 0.0001). After multivariate analysis, LA volumes, LASI, LA total strain (LA-εt) and LA-LGE were associated with increased risk of composite outcome (p < 0.001). Kaplan-Meier analysis showed significantly higher risk of composite endpoint for LA volumes (all p < 0.01), LASI > 0.725 (p < 0.003), and LA-εt < 30% (p < 0.0001). Stepwise Cox proportional-hazards models demonstrated a considerable incremental predictive value which resulted by adding LASI to LA-εt (Chi-square = 10.2, p < 0.001), and afterwards LA-LGE (Chi-Square = 15.8; p < 0.0001). NIDCM patients with defective LA volumes, LASI, LA-LGE and LA-εt had a higher risk for an outcome. LA-εt, LASI and LA-LGE provided independent incremental predictive value for outcome.

Artificial Intelligence and Antibiotic Discovery.

Over recent decades, a new antibiotic crisis has been unfolding due to a decreased research in this domain, a low return of investment for the companies that developed the drug, a lengthy and difficult research process, a low success rate for candidate molecules, an increased use of antibiotics in farms and an overall inappropriate use of antibiotics. This has led to a series of pathogens developing antibiotic resistance, which poses severe threats to public health systems while also driving up the costs of hospitalization and treatment. Moreover, without proper action and collaboration between academic and health institutions, a catastrophic trend might develop, with the possibility of returning to a pre-antibiotic era. Nevertheless, new emerging AI-based technologies have started to enter the field of antibiotic and drug development, offering a new perspective to an ever-growing problem. Cheaper and faster research can be achieved through algorithms that identify hit compounds, thereby further accelerating the development of new antibiotics, which represents a vital step in solving the current antibiotic crisis. The aim of this review is to provide an extended overview of the current artificial intelligence-based technologies that are used for antibiotic discovery, together with their technological and economic impact on the industrial sector.