Artificial Intelligence in Anesthesiology

Anesthesiology, Journal Year: 2019, Volume and Issue: 132(2), P. 379 - 394

Published: Sept. 15, 2019

Abstract Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection artificial and anesthesia research identified summarized six themes applications anesthesiology: (1) depth monitoring, (2) control anesthesia, (3) event risk prediction, (4) ultrasound guidance, (5) pain management, (6) operating room logistics. Based on papers review, several topics within were described summarized: machine learning (including supervised, unsupervised, reinforcement learning), techniques (e.g., classical learning, neural networks deep Bayesian methods), major applied intelligence. The implications for practicing anesthesiologist are discussed as its limitations role clinicians further developing use clinical care. potential to impact practice anesthesiology aspects ranging from perioperative support critical care delivery outpatient management.

Language: Английский

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Artificial intelligence (AI) and big data in cancer and precision oncology

Computational and Structural Biotechnology Journal, Journal Year: 2020, Volume and Issue: 18, P. 2300 - 2311

Published: Jan. 1, 2020

Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare sector. Advancement in technology has paved way for analysis big datasets a cost- time-effective manner. Clinical oncology research are reaping benefits AI. The burden cancer is global phenomenon. Efforts to reduce mortality rates requires early diagnosis effective therapeutic interventions. However, metastatic recurrent cancers evolve acquire drug resistance. It imperative detect novel biomarkers that induce resistance identify targets enhance treatment regimes. introduction next generation sequencing (NGS) platforms address these demands, revolutionised future precision oncology. NGS offers several clinical applications important risk predictor, detection disease, by medical imaging, accurate prognosis, biomarker identification discovery. generates large demand specialised bioinformatics resources analyse data relevant clinically significant. Through AI, diagnostics prognostic prediction enhanced with imaging delivers high resolution images. Regardless improvements technology, AI some challenges limitations, application remains be validated. By continuing progression innovation show great promise.

Language: Английский

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The Lancet Commission on diagnostics: transforming access to diagnostics

The Lancet, Journal Year: 2021, Volume and Issue: 398(10315), P. 1997 - 2050

Published: Oct. 7, 2021

At the end of 2019, first reports a new respiratory virus appeared in China. The subsequent COVID-19 pandemic has affected every person, country, world. One early lesson was crucial importance timely accurate diagnosis. A second widespread scarcity such diagnostic capacity and capability. supported findings 2018 Lancet Series on Pathology Laboratory Medicine Low-Income Middle-Income Countries, namely that despite diagnostics being central to health care, access testing pathology laboratory medicine (PALM) is poor inequitable many parts In imaging (DI), other major discipline, data are scarce, but what available suggest situation similar or even worse. Poor accessibility not issue. 2008, Maputo Declaration Strengthening Systems identified need address problems testing. Although progress been slow, there now conjunction factors potential accelerate change. First, three global priorities—universal coverage, antimicrobial resistance, security—all require better diagnostics. Second, publication an essential list (EDL) for priority settings by WHO key step recognising Third, greatly raised awareness Lastly, within past 15 years, extraordinary innovations technology informatics promise transformation across all aspects combination these can fuel political will This Commission Diagnostics set up with remit analysing issues identifying solutions both PALM DI, part because two disciplines because, increasingly, optimum patient care (eg, cancer) depends integration synthesis results disciplines. Also, share same issues; example, insufficient financial support, staff shortages, infrastructure problems, low visibility and, hence, priority. this Commission, we analyse current status use six building blocks systems, service delivery, workforce, information (analogous medicines), financing, leadership governance, as basis. Given dearth reliable comprehensive data, Commission's quantify, where possible, state globally. We tracer conditions (diabetes, hypertension, HIV, tuberculosis overall population, plus hepatitis B infection syphilis pregnant women) show gap (ie, proportion population condition who remain undiagnosed) is, at 35–62%, single largest pathway (the cascade comprising screening, diagnosis, treatment, cure successful management). also examine availability level facility, geography, socioeconomic group. most severe primary which only about 19% populations low-income lower-middle-income countries have simplest tests (other than those HIV malaria). Even hospitals, figure rises 60–70%. DI essentially absent outside hospitals. People poor, marginalised, young, less educated least Key messages147% little no diagnostics.2Diagnostics fundamental quality care. notion under-recognised, leading underfunding inadequate resources levels.3The so-called last mile particularly affects rural, marginalised communities globally; appropriate equity social justice.4The emphasised role without diagnostics, delivery universal resistance mitigation, preparedness cannot be achieved.5Innovations years areas technology, workforce) reduce gap, improve access, democratise empower patients.6As example impact, 1·1 million premature deaths middle-income could avoided annually reducing conditions: diabetes, women.7The economic case investment strong. median benefit–cost exceeds one five countries, four range 1·4:1 24:1.Given depth breadth sustained quality, affordable multi-decade prioritisation, commitment, investment. Incorporating into coverage packages begin process. 147% 24:1. Our conclusion just under half (47%) world's estimate from 35–62% 10% would annual number (LMICs) (2·5% total LMICs), disability-adjusted life-year (DALY) losses 38·5 (1·8% conditions). policy environment conclude cause prioritisation explicitly mentioned proposals largely missing national strategic plans health, focus National Action Plans Health Security limited primarily epidemic infectious diseases. corruption problem any system, susceptible they acquisition expensive equipment supplies. scarce operational level, necessary physical clearly deficient facilities, resulting weak services quality. Similarly, support capabilities, management procurement technical supply chains, widely insufficient. Regarding shortfall around 840 000 (using UK benchmark), noting education training enough maintain levels. Quality safety mechanisms standards LMICs. For 2019 study suggested India 1151 accredited medical laboratories, whereas USA, quarter India's 260 laboratories. Because explore how framework Shiffman Smith achieve With fresh people's minds pandemic, EDL (a useful tool way forward), might opportunity progress. offers associated developed evidence-based template basic core integrated tiered networks, designed meet needs predicted top 20 burden disease 2030 2040 GBD-20 EDL). enabler putative discuss technological innovation propose via changes policy, finance, infrastructure, proposed summarised following paragraphs relevant recommendation. outlines investing provide analysis aforementioned tests. costs relatively simple calculate, measuring benefits difficult context-specific, varying several factors, country income, prevalence, more effective treatment. work done area, making assumptions, LMICs one, strong There means technology) multiplicity challenges improving As solutions, 10 recommendations. each recommendation important its own right, highly interdependent. If implemented group, recommendations make substantial difference. relative absence it unsurprising countries. Therefore, recommend develop strategy do so evidenced-based network (this based our template) model (recommendation 1). allocated different system tiers: point-of-care investigations analysers x-ray first-level sophisticated MRI, CT, flow cytometers, nucleic acid analysers, microbial identification) higher facilities. Implementation serve drive staff, equipment, finance) system. existing facilities adapt their context. However, whatever adopted evidence-based. biggest provision entry point cascade, that, priority, (point-of-care ultrasound) made health-care centres 2). workforce expansion services. Expansion approaches alone New needed ensure contemporary skills, including competency-based education, expanded continuing professional development, telehealth remote services, greater task shifting sharing. expand size 3). Without systems questionable value, potentially causing harm wasting resources. regulatory addresses essential. Device regulation simplified regional harmonisation programmes prequalification. implementation accreditation competencies. develops governance 4). adequate always supporting improvement outlined Commission. These include efficient through management, pooled standardisation, fostering manufacturing capacity, development public–private partnerships manufacturers. additional financing generally essential, majority domestic public. Higher taxes tobacco (so-called sin taxes) possibility. Other sources instruments, Social Impact Bonds Development Bonds, rarely used borrowing multilateral banks. finance sustainable 5). Complementing improved international action increase affordability generally. Supporting production (market shaping) affordability. 6). reason why apposite time transformative identify broad relating offer greatest potential—namely, digitalisation, democratisation By enabling hospital self-testing self-sampling), patient, patients marginalised. To equity, privacy, alignment briefly review general principles implementation. designing technologies with, for, user, generating record monitoring indicators, standards-based approach interoperability conflict confusion. depend communications, well effect, main continued innovation, especially 7). particular challenge third living fragile situations. complex, challenging very actors involved. Within some challenges, coordination civilian security sector needed, humanitarian involved define 8). Considering probably barrier resourcing advocacy drive, combining efforts levels activities diverse stakeholders. coordinated programme levels, adopting World Assembly resolution 9). Finally, effort transforming focused, persistent, multi-year, sustainable, creation Alliance agencies promote 10). build next steps should initiation programmes, advocate, adoption integral programme. Continued research fill gaps; must turning point. over transform world close great does.

Language: Английский

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Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

Gut, Journal Year: 2020, Volume and Issue: 70(3), P. 544 - 554

Published: July 20, 2020

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), grading is a poor predictor of disease progression, and consensus subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis cost-effective tool associate complex features tissue organisation with outcome data resolve unclassifiable heterogeneous cases. this study, we present an image-based approach predict CRC CMS from standard H&E sections using deep learning. Design Training evaluation neural network were performed total n=1206 comprehensive multi-omic three independent datasets (training FOCUS trial, n=278 patients; test rectal biopsies, GRAMPIAN cohort, n=144 The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth calls ascertained by matching random forest single sample predictions classifier. Results Image-based (imCMS) accurately classified unseen TCGA (n=431 slides, AUC)=0.84) biopsies (n=265 AUC=0.85). imCMS spatially resolved intratumoural heterogeneity provided secondary correlating bioinformatic prediction data. samples previously RNA profiling, reproduced expected correlations genomic epigenetic alterations showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows classifiers can made images, opening door simple, cheap reliable stratification within routine workflows.

Language: Английский

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Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare

Journal of Personalized Medicine, Journal Year: 2023, Volume and Issue: 13(8), P. 1214 - 1214

Published: July 31, 2023

Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist diagnosis, treatment selection, patient monitoring, enabling more accurate efficient healthcare delivery. The widespread implementation role to revolutionize patients' outcomes transform way is practiced, leading improved accessibility, affordability, quality care. This article explores diverse applications reviews current state adoption healthcare. It concludes by emphasizing need for collaboration between physicians experts harness full AI.

Language: Английский

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Artificial intelligence in the management of metabolic disorders: a comprehensive review

Journal of Endocrinological Investigation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Language: Английский

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Complete Digital Pathology for Routine Histopathology Diagnosis in a Multicenter Hospital Network

Archives of Pathology & Laboratory Medicine, Journal Year: 2019, Volume and Issue: 144(2), P. 221 - 228

Published: July 11, 2019

Context.— Complete digital pathology and whole slide imaging for routine histopathology diagnosis is currently in use few laboratories worldwide. Granada University Hospitals, Spain, which comprises 4 hospitals, adopted full primary 2016. Objective.— To describe the methodology resulting experience at Hospitals transitioning to diagnosis. Design.— All glass slides generated were digitized ×40 using Philips IntelliSite Pathology Solution, includes an ultrafast scanner image management system. hematoxylin-eosin–stained preparations immunohistochemistry histochemistry digitized. The existing sample-tracking software system integrated allow data interchange through Health Level 7 protocol. Results.— Circa 160 000 specimens have been signed out This more than 800 slides. scanning error rate during implementation phase was below 1.5%, subsequent workflow optimization rendered this negligible. Since implementation, pathologists 21% cases per year on average. Conclusions.— Digital adequate medium Successful digitization relies sample tracking integration of information technology infrastructure. Rapid reliable equivalent key transition a fully workflow. resulted efficiency gains preanalytical analytical phases, created basis computational pathology: computer-assisted tools aid

Language: Английский

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Precision immunoprofiling by image analysis and artificial intelligence

Virchows Archiv, Journal Year: 2018, Volume and Issue: 474(4), P. 511 - 522

Published: Nov. 23, 2018

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection stratification. This requirement can be met by a combination computational pathology artificial intelligence. Here, we critically assess approaches supporting development standardized methodology in assessment immune-oncology biomarkers, such as PD-L1 immune cell infiltrates. We examine immunoprofiling through spatial analysis tumor-immune interactions multiplexing technologies predictor response cancer treatment. Further, discuss how integrated bioinformatics enable amalgamation complex morphological phenotypes with multiomics datasets that drive precision medicine. provide an outline machine learning (ML) intelligence tools illustrate fields application immune-oncology, pattern-recognition large deep survival analysis. Synergies surgical analyses are expected improve stratification immuno-oncology. propose future clinical demands will best (1) dedicated research at interface bioinformatics, supported professional societies, (2) integration data sciences digital image education pathologists.

Language: Английский

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The future of pathology is digital

Pathology - Research and Practice, Journal Year: 2020, Volume and Issue: 216(9), P. 153040 - 153040

Published: June 20, 2020

Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability cases' referral information, as needed for example, the pathologist, this will be increased future even more. In radiology, established standards define information models, data transmission mechanisms, workflows. Other disciplines, such pathology, cardiology, radiation therapy, now further demands addition to these standards. Pathology may have highest technical on systems, very complex workflows, digitization slides generating enormous amounts up Gigabytes per biopsy. This requires generated biopsy, gigabyte range. Digital pathology allows a change from classical histopathological diagnosis microscopes glass virtual microscopy computer, multiple tools using intelligence machine learning pathologists work.

Language: Английский

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Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective

Journal of Clinical Pathology, Journal Year: 2020, Volume and Issue: 74(7), P. 443 - 447

Published: July 3, 2020

The measures to control the COVID-19 outbreak will likely remain a feature of our working lives until suitable vaccine or treatment is found. pandemic has had substantial impact on clinical services, including cancer pathways. Pathologists are remotely in many circumstances protect themselves, colleagues, family members and delivery services. effects research trials have also been significant with changes protocols, suspensions studies redeployment resources COVID-19. In this article, we explore specific academic pathology how digital artificial intelligence can play key role safeguarding services pathology-based current climate future.

Language: Английский

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