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1.
J Environ Manage ; 301: 113810, 2022 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-34731959

RESUMEN

Sewer networks play a pivotal role in our everyday lives by transporting the stormwater and urban sewage away from the urban areas. In this regard, Sewer Overflow (SO) has been considered as a detrimental threat to our environment and health, which results from the wastewater discharge into the environment. In order to grapple with such deleterious phenomenon, numerous studies have been conducted; however, there has not been any review paper that provides the researchers undertaking research in this area with the following inclusive picture: (1) detailed-scientometric analysis of the research undertaken hitherto, (2) the types of methodologies used in the previous studies, (3) the aspects of environment impacted by the SO occurrence, and (4) the gaps existing in the relative literature together with the potential future works to be undertaken. Based on the comprehensive review undertaken, it is observed that simulation and artificial intelligence-based methods have been the most popular approaches. In addition, it has come to the attention that the detrimental impacts associated with the SO are fourfold as follows: air, quality of water, soil, and business and structure. Among these, the majority of the studies' focus have been tilted towards the impact of SO on the quality of ground water. The outcomes of this state-of-the-art review provides the researchers and environmental engineers with inclusive hindsight in dealing with such serious issue, which in turn, this culminates in a significant improvement in our environment as well as humans' well-beings.


Asunto(s)
Inteligencia Artificial , Agua Subterránea , Humanos , Aguas del Alcantarillado , Aguas Residuales
2.
Med Gas Res ; 12(2): 60-66, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-34677154

RESUMEN

The coronavirus disease 2019 (COVID-19) epidemic went down in history as a pandemic caused by corona-viruses that emerged in 2019 and spread rapidly around the world. The different symptoms of COVID-19 made it difficult to understand which variables were more influential on the diagnosis, course and mortality of the disease. Machine learning models can accurately assess hidden patterns among risk factors by analyzing large-datasets to quickly predict diagnosis, prognosis and mortality of diseases. Because of this advantage, the use of machine learning models as decision support systems in health services is increasing. The aim of this study is to determine the diagnosis and prognosis of COVID-19 disease with blood-gas data using the Chi-squared Automatic Interaction Detector (CHAID) decision-tree-model, one of the machine learning methods, which is a subfield of artificial intelligence. This study was carried out on a total of 686 patients with COVID-19 (n = 343) and non-COVID-19 (n = 343) treated at Erzincan-Mengücek-Gazi-Training and Research-Hospital between April 1, 2020 and March 1, 2021. Arterial blood gas values of all patients were obtained from the hospital registry system. While the total-accuracyratio of the decision-tree-model was 65.0% in predicting the prognosis of the disease, it was 68.2% in the diagnosis of the disease. According to the results obtained, the low ionized-calcium value (< 1.10 mM) significantly predicted the need for intensive care of COVID-19 patients. At admission, low-carboxyhemoglobin (< 1.00%), high-pH (> 7.43), low-sodium (< 135.0 mM), hematocrit (< 40.0%), and methemoglobin (< 1.30%) values are important biomarkers in the diagnosis of COVID-19 and the results were promising. The findings in the study may aid in the early-diagnosis of the disease and the intensive-care treatment of patients who are severe. The study was approved by the Ministry of Health and Erzincan University Faculty of Medicine Clinical Research Ethics Committee.


Asunto(s)
Inteligencia Artificial , COVID-19 , Árboles de Decisión , Humanos , Aprendizaje Automático , Pronóstico , SARS-CoV-2
3.
Appl Ergon ; 98: 103599, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34656892

RESUMEN

A large component of Neville Stanton's work has focused on situation awareness in domains such as defence, transport, and process control. A significant contribution has been to initiate a shift from considering individual human operator situation awareness to considering the situation awareness of human and non-human teams, organisations, and even sociotechnical systems. Though controversial when introduced, the distributed situation awareness model has become increasingly relevant for modern day systems and problems. In this article we reflect on Stanton's contribution and point to a pressing need to consider a. The situation awareness of advanced technologies, and b. situation awareness at a sociotechnical system, societal and even global level. This is demonstrated via discussion on two contemporaneous issues: automated vehicles and the COVID-19 pandemic. It is concluded that, given advances such as artificial intelligence, the increased connectedness of society, emerging issues such as disinformation, and an increasing set of global threats, Stanton's distributed situation awareness model and associated analysis framework provide a useful toolkit for future Human Factors and Ergonomics applications.


Asunto(s)
Concienciación , COVID-19 , Inteligencia Artificial , Humanos , Pandemias , SARS-CoV-2
4.
Methods Mol Biol ; 2390: 103-112, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-34731465

RESUMEN

The development of vaccines for the treatment of COVID-19 is paving the way for new hope. Despite this, the risk of the virus mutating into a vaccine-resistant variant still persists. As a result, the demand of efficacious drugs to treat COVID-19 is still pertinent. To this end, scientists continue to identify and repurpose marketed drugs for this new disease. Many of these drugs are currently undergoing clinical trials and, so far, only one has been officially approved by FDA. Drug repurposing is a much faster route to the clinic than standard drug development of novel molecules, nevertheless in a pandemic this process is still not fast enough to halt the spread of the virus. Artificial intelligence has already played a large part in hastening the drug discovery process, not only by facilitating the selection of potential drug candidates but also in monitoring the pandemic and enabling faster diagnosis of patients. In this chapter, we focus on the impact and challenges that artificial intelligence has demonstrated thus far with respect to drug repurposing of therapeutics for the treatment of COVID-19.


Asunto(s)
Antivirales/uso terapéutico , Inteligencia Artificial , COVID-19/tratamiento farmacológico , Descubrimiento de Drogas , Reposicionamiento de Medicamentos , SARS-CoV-2/efectos de los fármacos , Animales , Antivirales/efectos adversos , COVID-19/diagnóstico , COVID-19/virología , Interacciones Huésped-Patógeno , Humanos , Aprendizaje Automático , Estructura Molecular , SARS-CoV-2/patogenicidad , Relación Estructura-Actividad
5.
Talanta ; 237: 122901, 2022 Jan 15.
Artículo en Inglés | MEDLINE | ID: mdl-34736716

RESUMEN

Raman spectroscopy combined with artificial intelligence algorithms have been widely explored and focused on in recent years for food safety testing. It is still a challenge to overcome the cumbersome culture process of bacteria and the need for a large number of samples, which hinder qualitative analysis, to obtain a high classification accuracy. In this paper, we propose a method based on Raman spectroscopy combined with generative adversarial network and multiclass support vector machine to classify foodborne pathogenic bacteria. 30,000 iterations of generative adversarial network are trained for three strains of bacteria, generative model G generates data similar to the actual samples, discriminant model D verifies the accuracy of the generated data, and 19 feature variables are obtained by selecting the feature bands according to the Raman spectroscopy pattern. Better classification results are obtained by optimising the parameters of the multi-class support vector machine, etc. Our detection and classification method not only solves the problem of needing a large number of samples as training set, but also improves the accuracy of the classification model. Therefore, this GAN-SVM classification model provides a new idea for the detection of bacteria based on Raman spectroscopy technology combined with artificial intelligence algorithms.


Asunto(s)
Espectrometría Raman , Máquina de Vectores de Soporte , Algoritmos , Inteligencia Artificial , Bacterias
6.
Appl Ergon ; 98: 103556, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34419785

RESUMEN

The high prevalence of work-related musculoskeletal disorders (WRMSDs) has been a concern in the meat-processing industry, owing to the manual nature of the work and the high upper-limb and neck exposure to movements that can lead to WRMSD. The ability to perform an accurate and fast assessment of WRMSDs remains a challenge in industrial environments. Most assessment methodologies rely on standard survey-based methods, which are time- and labor-intensive. In this paper, we present an application of inertial measurement units (IMUs) to measure human activity, and the use of artificial intelligence and machine learning techniques to perform task classification and ergonomic assessments in workplace settings. We present the results obtained by using simple low-cost IMUs worn on slaughterhouse worker wrists to capture information on their movements. We describe the use of this information to detect the risk factors of the wrists/hands that can lead to WRMSDs. The results indicate that by using low-cost IMU-based sensors on the wrists of slaughterhouse workers, we can accurately classify the sharpness of the knife and predict the worker RULA score.


Asunto(s)
Mataderos , Enfermedades Musculoesqueléticas , Inteligencia Artificial , Ergonomía , Humanos , Aprendizaje Automático , Enfermedades Musculoesqueléticas/diagnóstico
7.
Urol Clin North Am ; 49(1): 65-117, 2022 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-34776055

RESUMEN

The growth and adoption of artificial intelligence has led to impressive results in urology. As artificial intelligence grows more ubiquitous, it is important to establish artificial intelligence literacy in the workforce. To this end, we present a narrative review of the literature of artificial intelligence and machine learning in urology and propose a checklist of reporting standards to improve readability and evaluate the current state of the literature. The listed article demonstrated heterogeneous reporting of methodologies and outcomes, limiting generalizability of research. We hope that this review serves as a foundation for future evaluation of medical research in artificial intelligence.


Asunto(s)
Inteligencia Artificial , Proyectos de Investigación/normas , Neoplasias Urológicas/diagnóstico , Investigación Biomédica , Humanos , Hidronefrosis/diagnóstico , Cálculos Renales/diagnóstico , Cálculos Renales/cirugía , Pronóstico , Neoplasias Urológicas/terapia , Urólogos , Reflujo Vesicoureteral/cirugía
8.
Crit Care Clin ; 38(1): 129-139, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34794627

RESUMEN

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.


Asunto(s)
Inteligencia Artificial , Sistemas de Apoyo a Decisiones Clínicas , Cuidados Críticos , Humanos , Unidades de Cuidados Intensivos , Aprendizaje Automático
9.
Magn Reson Imaging Clin N Am ; 30(1): 81-94, 2022 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-34802583

RESUMEN

Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.


Asunto(s)
Aprendizaje Profundo , Neoplasias de Cabeza y Cuello , Algoritmos , Inteligencia Artificial , Neoplasias de Cabeza y Cuello/diagnóstico por imagen , Humanos , Imagen por Resonancia Magnética
10.
Ann Ig ; 34(1): 79-83, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-33797549

RESUMEN

Abstract: Nowadays, digital information has increased exponentially in every field to such an extent that it generates huge amounts of electronic data, namely Big Data. In the field of Artificial Intelligence, Machine Learning can be exploited in order to transform the large amount of information to improve decision-making. We retrospectively evaluated the data collected from 2016 to 2018, using the database of approximately 4000 rehabilitation hospital discharges (SDO) of the Latium Region (Italy). Three models of machine learning algorithms were considered: Support of vector machine; Neural networks; Random forests. Applying this model, the estimate of the average error is 9.077, and specifically, considering the distinction between orthopedic and neurological patients, the average error obtained is 7.65 for orthopedic and 10.73 for neurological patients. SDO information flow can be used to represent and quantify the potential inadequacy and inefficiency of rehabilitation hospitalizations, although there are limitations such as the absence of description of pre-pathological conditions, changes in health status from the beginning to the end of hospitalization, specific short- and long-term outcomes of rehabilitation, services provided during hospitalization, as well as psycho-social variables. Furthermore, information from wearable devices capable of providing clinical parameters and movement data could be integrated into the dataset.


Asunto(s)
Inteligencia Artificial , Aprendizaje Automático , Algoritmos , Bases de Datos Factuales , Humanos , Estudios Retrospectivos
11.
Sci Total Environ ; 803: 149834, 2022 Jan 10.
Artículo en Inglés | MEDLINE | ID: mdl-34525746

RESUMEN

A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.


Asunto(s)
COVID-19 , Pandemias , Inteligencia Artificial , Brotes de Enfermedades , Humanos , Aprendizaje Automático , SARS-CoV-2 , Aguas Residuales , Monitoreo Epidemiológico Basado en Aguas Residuales
12.
Spectrochim Acta A Mol Biomol Spectrosc ; 264: 120250, 2022 Jan 05.
Artículo en Inglés | MEDLINE | ID: mdl-34391991

RESUMEN

Botanical drugs hold great potential to prevent and treat complex diseases. Quality control is essential in ensuring the safety, efficacy, and therapeutic consistency of these drug products. The quality of a botanical drug product can be assessed using a variety of analytical methods based on criteria that judge the identity, strength, purity, and potency. However, most of these methods are developed on separate analytical platforms, and few approaches are available for in-process monitoring of multiple quality properties in a non-destructive manner. Here, we present a hyperspectral imaging-based strategy for online measurement of physical, chemical, and biological properties of botanical drugs using artificial intelligence algorithms. An end-to-end convolutional neural network (CNN) model was established to accurately determine phytochemicals and bioactivities based on the spectra. Besides, a new dual-scale anomaly (DSA) detection algorithm was proposed for visible particle inspection based on the images. The strategy was exemplified on Shuxuening Injection, a Ginkgo biloba-derived drug used in the treatment of cerebrovascular and cardiovascular diseases. Four quality metrics of the injection, including total flavonol, total ginkgolides, antioxidant activity, and anticoagulant activity, were successfully predicted by the CNN model with validation R2 of 0.922, 0.921, 0.880, and 0.913 respectively, showing better performance than the other models. Unqualified samples with visible particles could be detected by DSA with a low false alarm rate of 9.38 %. Chromaticity results indicated that the inter-company variations of color were significant, while intra-company variations were relatively small. This demonstrates a real application of integrating hyperspectral imaging with artificial intelligence to provide a rapid, accurate, and non-destructive approach for process analysis of botanical drugs.


Asunto(s)
Inteligencia Artificial , Imágenes Hiperespectrales , Algoritmos , Redes Neurales de la Computación , Control de Calidad
13.
Gastrointest Endosc Clin N Am ; 32(1): 59-74, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34798987

RESUMEN

Screening for colorectal cancer (CRC) in Lynch syndrome enables early detection and likely cancer prevention. CRC screening guidelines have evolved from universal to gene-specific recommendations based on lifetime neoplasia risks. Regular screening for Lynch syndrome reduces CRC-related mortality; however, high CRC incidence during regular colonoscopy screening suggests the possibility of nonpolypoid carcinogenesis. Colonoscopy is the primary modality for screening for Lynch syndrome with mixed and emerging data on quality metrics, chromoendoscopy, artificial intelligence, and nonendoscopic modalities. Screening adherence varies across studies. In this review, we present the current state of CRC screening recommendations, outcomes, and modalities in Lynch syndrome.


Asunto(s)
Neoplasias Colorrectales Hereditarias sin Poliposis , Neoplasias Colorrectales , Inteligencia Artificial , Colonoscopía , Neoplasias Colorrectales/diagnóstico , Neoplasias Colorrectales/epidemiología , Neoplasias Colorrectales Hereditarias sin Poliposis/diagnóstico , Neoplasias Colorrectales Hereditarias sin Poliposis/genética , Detección Precoz del Cáncer , Humanos
14.
PET Clin ; 17(1): 1-12, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34809860

RESUMEN

Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.


Asunto(s)
Inteligencia Artificial , Ecosistema , Diagnóstico por Imagen , Humanos
15.
PET Clin ; 17(1): 115-135, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34809861

RESUMEN

This review discusses the current state of artificial intelligence (AI) in 18F-NaF-PET/CT imaging and the potential applications to come in diagnosis, prognostication, and improvement of care in patients with bone diseases, with emphasis on the role of AI algorithms in CT bone segmentation, relying on their prevalence in medical imaging and utility in the extraction of spatial information in combined PET/CT studies.


Asunto(s)
Enfermedades Óseas , Fluoruro de Sodio , Inteligencia Artificial , Radioisótopos de Flúor , Humanos , Tomografía Computarizada por Tomografía de Emisión de Positrones , Tomografía de Emisión de Positrones , Radiofármacos
16.
PET Clin ; 17(1): 13-29, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34809862

RESUMEN

Almost 1 in 10 individuals can suffer from one of many rare diseases (RDs). The average time to diagnosis for an RD patient is as high as 7 years. Artificial intelligence (AI)-based positron emission tomography (PET), if implemented appropriately, has tremendous potential to advance the diagnosis of RDs. Patient advocacy groups must be active stakeholders in the AI ecosystem if we are to avoid potential issues related to the implementation of AI into health care. AI medical devices must not only be RD-aware at each stage of their conceptualization and life cycle but also should be trained on diverse and augmented datasets representative of the end-user population including RDs. Inability to do so leads to potential harm and unsustainable deployment of AI-based medical devices (AIMDs) into clinical practice.


Asunto(s)
Inteligencia Artificial , Enfermedades Raras , Ecosistema , Humanos , Tomografía de Emisión de Positrones , Radiografía , Enfermedades Raras/diagnóstico por imagen
17.
PET Clin ; 17(1): 137-143, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34809863

RESUMEN

PET imaging with targeted novel tracers has been commonly used in the clinical management of prostate cancer. The use of artificial intelligence (AI) in PET imaging is a relatively new approach and in this review article, we will review the current trends and categorize the currently available research into the quantification of tumor burden within the organ, evaluation of metastatic disease, and translational/supplemental research which aims to improve other AI research efforts.


Asunto(s)
Inteligencia Artificial , Neoplasias de la Próstata , Humanos , Masculino , Tomografía de Emisión de Positrones , Neoplasias de la Próstata/diagnóstico por imagen
18.
PET Clin ; 17(1): 145-174, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34809864

RESUMEN

Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.


Asunto(s)
Inteligencia Artificial , Linfoma , Fluorodesoxiglucosa F18 , Humanos , Linfoma/diagnóstico por imagen
19.
PET Clin ; 17(1): 175-182, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34809865

RESUMEN

Artificial intelligence (AI) has been widely used throughout medical imaging, including PET, for data correction, image reconstruction, and image processing tasks. However, there are number of opportunities for the application of AI in photon detector performance or the data collection process, such as to improve detector spatial resolution, time-of-flight information, or other PET system performance characteristics. This review outlines current topics, research highlights, and future directions of AI in PET instrumentation.


Asunto(s)
Inteligencia Artificial , Tomografía de Emisión de Positrones , Humanos , Procesamiento de Imagen Asistido por Computador , Radiografía
20.
PET Clin ; 17(1): 183-212, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34809866

RESUMEN

Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.


Asunto(s)
Inteligencia Artificial , Neoplasias , Diagnóstico por Imagen , Humanos , Neoplasias/diagnóstico por imagen , Tomografía Computarizada por Tomografía de Emisión de Positrones , Pronóstico
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