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1.
Rev. bras. med. esporte ; 29: e2022_0152, 2023. tab, graf
Artículo en Inglés | LILACS | ID: biblio-1394837

RESUMEN

ABSTRACT Introduction: In today's rapid development of science and technology, digital network data mining technology is developing as fast as the expansion of the frontiers of science and technology allows, with a very broad application level, covering most of the civilized environment. However, there is still much to explore in the application of sports training. Objective: Analyze the feasibility of data mining based on the digital network of sports training, maximizing athletes' training. Methods: This paper uses the experimental analysis of human FFT, combined with BP artificial intelligence network and deep data mining technology, to design a new sports training environment. The controlled test of this model was designed to compare advanced athletic training modalities with traditional modalities, comparing the athletes' explosive power, endurance, and fitness. Results: After 30 days of physical training, the athletic strength of athletes with advanced fitness increased by 15.33%, endurance increased by 15.85%, and fitness increased by 14.23%. Conclusion: The algorithm designed in this paper positively impacts maximizing athletes' training. It may have a favorable impact on training outcomes, as well as increase the athlete's interest in the sport. Level of evidence II; Therapeutic studies - investigating treatment outcomes.


RESUMO Introdução: No rápido desenvolvimento atual de ciência e tecnologia, a tecnologia de mineração de dados de rede digital desenvolve-se tão rápido quanto a expansão das fronteiras da ciência e tecnologia permitem, com um nível de aplicação muito amplo, cobrindo a maior parte do ambiente civilizado. No entanto, ainda há muito para explorar da aplicação no treinamento esportivo. Objetivo: Análise de viabilidade da mineração de dados com base na rede digital da formação esportiva, maximizar o treinamento dos atletas. Métodos: Este trabalho utiliza a análise experimental da FFT humana, combinada com a rede de inteligência artificial da BP e tecnologia de mineração profunda de dados, para projetar um novo ambiente de treinamento esportivo. O teste controlado deste modelo foi projetado para comparar modalidades avançadas de treinamento atlético com as modalidades tradicionais, comparando o poder explosivo, resistência e condição física do atleta. Resultados: Após 30 dias de treinamento físico, a força atlética dos esportistas com aptidão física avançada aumentou 15,33%, a resistência aumentou 15,85%, e o condicionamento físico aumentou 14,23%. Conclusão: O algoritmo desenhado neste artigo tem um impacto positivo na maximização do treinamento dos atletas. Pode ter um impacto favorável nos resultados do treinamento, bem como aumentar o interesse do atleta pelo esporte. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.


RESUMEN Introducción: En el rápido desarrollo actual de la ciencia y la tecnología, la tecnología de extracción de datos de redes digitales se desarrolla tan rápido como lo permiten las fronteras en expansión de la ciencia y la tecnología, con un nivel de aplicación muy amplio que abarca la mayor parte del entorno civilizado. Sin embargo, aún queda mucho por explorar de la aplicación en el entrenamiento deportivo. Objetivo: Análisis de viabilidad de la minería de datos basada en la red digital de entrenamiento deportivo, maximizar la formación de los atletas. Métodos: Este trabajo utiliza el análisis experimental de la FFT humana, combinado con la red de inteligencia artificial BP y la tecnología de minería de datos profunda, para diseñar un nuevo entorno de entrenamiento deportivo. La prueba controlada de este modelo se diseñó para comparar las modalidades de entrenamiento atlético avanzado con las modalidades tradicionales, comparando la potencia explosiva, la resistencia y la forma física del atleta. Resultados: Después de 30 días de entrenamiento físico, la fuerza atlética de los atletas con un estado físico avanzado aumentó en un 15,33%, la resistencia aumentó en un 15,85% y el estado físico aumentó en un 14,23%. Conclusión: El algoritmo diseñado en este trabajo tiene un impacto positivo en la maximización del entrenamiento de los atletas. Puede tener un impacto favorable en los resultados del entrenamiento, así como aumentar el interés del atleta por el deporte. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.


Asunto(s)
Humanos , Inteligencia Artificial , Aptitud Física/fisiología , Redes Neurales de la Computación , Rendimiento Atlético/fisiología , Atletas
3.
Radiographics ; 43(1): e220060, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-36331878

RESUMEN

The use of digital breast tomosynthesis (DBT) in breast cancer screening has become widely accepted, facilitating increased cancer detection and lower recall rates compared with those achieved by using full-field digital mammography (DM). However, the use of DBT, as compared with DM, raises new challenges, including a larger number of acquired images and thus longer interpretation times. While most current artificial intelligence (AI) applications are developed for DM, there are multiple potential opportunities for AI to augment the benefits of DBT. During the diagnostic steps of lesion detection, characterization, and classification, AI algorithms may not only assist in the detection of indeterminate or suspicious findings but also aid in predicting the likelihood of malignancy for a particular lesion. During image acquisition and processing, AI algorithms may help reduce radiation dose and improve lesion conspicuity on synthetic two-dimensional DM images. The use of AI algorithms may also improve workflow efficiency and decrease the radiologist's interpretation time. There has been significant growth in research that applies AI to DBT, with several algorithms approved by the U.S. Food and Drug Administration for clinical implementation. Further development of AI models for DBT has the potential to lead to improved practice efficiency and ultimately improved patient health outcomes of breast cancer screening and diagnostic evaluation. See the invited commentary by Bahl in this issue. ©RSNA, 2022.


Asunto(s)
Inteligencia Artificial , Neoplasias de la Mama , Humanos , Femenino , Mamografía/métodos , Detección Precoz del Cáncer/métodos , Neoplasias de la Mama/patología , Algoritmos , Mama/diagnóstico por imagen
4.
Crit Care Clin ; 39(1): 235-242, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-36333034

RESUMEN

In recent years, the volume of digitalized web-based information utilizing modern computer-based technology for data storage, processing, and analysis has grown rapidly. Humans can process a limited number of variables at any given time. Thus, the deluge of clinically useful information in the intensive care unit environment remains untapped. Innovations in machine learning technology with the development of deep neural networks and efficient, cost-effective data archival systems have provided the infrastructure to apply artificial intelligence on big data for determination of clinical events and outcomes. Here, we introduce a few computer-based technologies that have been tested across these domains.


Asunto(s)
Inteligencia Artificial , Macrodatos , Humanos , Ciencia de los Datos , Redes Neurales de la Computación , Aprendizaje Automático
5.
Methods Mol Biol ; 2575: 195-237, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-36301477

RESUMEN

Meeting medical challenges posed by global burdens is proven to be of primary interest. One example is the COVID-19 epidemic that humankind is currently experiencing, since around December 2019. Innovation is key to respond rapidly and effectively to sanitary and health emergencies, when human lives are severely threatened. In this scenery, medical devices that can be rapidly launched in the market and manufactured at scale are crucial for saving lives. One example is a lifesaving respiratory device launched in about 10 days (Mercedes F1 team's new device based on continuous positive airway pressure devices) and rapidly approved by international agencies responsible for assuring drug and medical devices safety, in response to the COVID-19 burden. Remarkably, it is the first time in history that mankind observes disease spread reaching such high proportions, globally, in such short time scale. However, while this epidemic had, in March 2020, reached the critical figures of about 38,000 deaths and c. 738,000 infected, organ donation and transplantation patients are suffering for years, accounting for an increasing number of affected, annually. These patients are invisible for the general public. Therefore, this chapter approaches the organ donation and transplantation burden, proposing effective solutions to leverage the suffering, improving life quality of patients enduring several underlying issues, from hemodialysis complications and critical organ failure to lacking compatible donors. This, on the basis of technology repurposing, to speed up approval processes followed by international agencies responsible for assuring drug and medical devices safety, while adding innovative methods to existing technology and reducing invasiveness.


Asunto(s)
COVID-19 , Trasplante de Órganos , Obtención de Tejidos y Órganos , Humanos , Donantes de Tejidos , Nanotecnología , Inteligencia Artificial
6.
Magn Reson Imaging ; 95: 1-11, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-36241031

RESUMEN

Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challenging to obtain high-quality MR images. In this article, we propose a method of using artificial intelligence to expand the coils to achieve the goal of generating the virtual coils. The main characteristic of our work is utilizing dummy variable technology to expand/extrapolate the receive coils in both image and k-space domains. The high-dimensional information formed by coil expansion is used as the prior information to improve the reconstruction performance of parallel imaging. Two main components are incorporated into the network design, namely variable augmentation technology and sum of squares (SOS) objective function. Variable augmentation provides the network with more high-dimensional prior information, which is helpful for the network to extract the deep feature information of the data. The SOS objective function is employed to solve the deficiency of k-space data training while speeding up convergence. Experimental results demonstrated its great potentials in accelerating parallel imaging reconstruction.


Asunto(s)
Inteligencia Artificial , Aprendizaje Profundo , Imagen por Resonancia Magnética/métodos , Procesamiento de Imagen Asistido por Computador/métodos
7.
J Affect Disord ; 321: 272-278, 2023 Jan 15.
Artículo en Inglés | MEDLINE | ID: mdl-36280197

RESUMEN

BACKGROUND: Major depressive disorder (MDD) is largely managed in primary care, but physicians vary widely in their understanding of symptoms and treatments. This study aims to better understand the evolution of depression from initial diagnosis over a 3-year period. METHODS: This was a noninterventional, retrospective, longitudinal study, with 2 waves of participant interviews approximately 3 years apart. Phone interviews were conducted using the hybrid artificial intelligence (AI) Sleep-EVAL system, an AI-driven diagnostic deep learning tool. Participants were noninstitutionalized adults representative of the general population in 8 US states. Diagnosis was confirmed according to the DSM-5 using the Sleep-EVAL System. RESULTS: 10,931 participants completed Wave 1 and 2 (W1, W2) interviews. The prevalence of MDD, including partial and complete remission, was 13.4 % and 19.6 % in W1 and W2, respectively. About 42 % of MDD participants at W1 continued to report depressive symptoms at W2. Approximately half of antidepressant (AD) users in W1 were moderately to completely dissatisfied with their treatment; 29.6 % changed their AD for a different one, with 16.4 % switching from one SSRI to another between W1 and W2. Primary care physicians were the top AD prescribers, both in W1 (45.7 %) and W2 (59%), respectively. LIMITATIONS: Data collected relied on self-reporting by participants. As such, the interpretation of the data may be limited. CONCLUSIONS: Depression affects a sizeable portion of the US population. Dissatisfaction with treatment, frequent switching of ADs, and changing care providers are associated with low rates of remission. Residual symptoms remain a challenge that future research must address.


Asunto(s)
Trastorno Depresivo Mayor , Humanos , Adulto , Estudios Longitudinales , Trastorno Depresivo Mayor/diagnóstico , Trastorno Depresivo Mayor/tratamiento farmacológico , Trastorno Depresivo Mayor/epidemiología , Depresión , Estudios Retrospectivos , Inteligencia Artificial , Antidepresivos/uso terapéutico
8.
Sci Total Environ ; 855: 158439, 2023 Jan 10.
Artículo en Inglés | MEDLINE | ID: mdl-36113788

RESUMEN

Tumours are nowadays the second world­leading cause of death after cardiovascular diseases. During the last decades of cancer research, lifestyle and random/genetic factors have been blamed for cancer mortality, with obesity, sedentary habits, alcoholism, and smoking contributing as supposed major causes. However, there is an emerging consensus that environmental pollution should be considered one of the main triggers. Unfortunately, all this preliminary scientific evidence has not always been followed by governments and institutions, which still fail to pursue research on cancer's environmental connections. In this unprecedented national-scale detailed study, we analyzed the links between cancer mortality, socio-economic factors, and sources of environmental pollution in Italy, both at wider regional and finer provincial scales, with an artificial intelligence approach. Overall, we found that cancer mortality does not have a random or spatial distribution and exceeds the national average mainly when environmental pollution is also higher, despite healthier lifestyle habits. Our machine learning analysis of 35 environmental sources of pollution showed that air quality ranks first for importance concerning the average cancer mortality rate, followed by sites to be reclaimed, urban areas, and motor vehicle density. Moreover, other environmental sources of pollution proved to be relevant for the mortality of some specific cancer types. Given these alarming results, we call for a rearrangement of the priority of cancer research and care that sees the reduction and prevention of environmental contamination as a priority action to put in place in the tough struggle against cancer.


Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Neoplasias , Humanos , Inteligencia Artificial , Contaminación Ambiental/efectos adversos , Vehículos a Motor , Italia/epidemiología , Exposición a Riesgos Ambientales , Mortalidad
9.
J Affect Disord ; 320: 37-41, 2023 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-36162682

RESUMEN

BACKGROUND: Suicide messages can be transmitted infinitely online; the Internet is influential in suicide prevention. Identifying suicide risks online via artificial technological advances may help predict suicide. METHODS: We built a classifier that detects open messages containing suicidal ideation or behavior-related words in social media via text mining methods and developed the Monitoring-Tracking-Rescuing model, which links data monitoring and tracking to high-risk suicide rescues. Natural language processing (NLP) techniques such as Long Short-Term Memory and Bidirectional Encoder Representations from Transformers were applied to online posts of common social media sites in Taiwan. This model uses a two-step high-risk identification procedure: an automatic prediction process using NLP to classify suicide-risk levels, followed by professional validation by a senior psychiatrist and a nursing faculty specialized in suicidology. RESULTS: From a dataset containing 404 high-risk and 2226 no- or low-risk articles, the sensitivity and specificity of our model reached 80 %. LIMITATIONS: The model is limited to data platforms that can be "crawled" and excludes suicide-risk content from graphics, video and audio files. Additionally, machine learning does not provide the best recognition rate from complex online messages. Keywords for high-risk suicide in long articles are difficult to interpret using this model. Finally, the model lacks keywords for suicide-protective factors. CONCLUSIONS: Artificial intelligence techniques may help detect and monitor high-risk suicide posts and inform mental health professionals of these posts. Periodic tracking plus manual validation to determine risk levels are recommended to enhance the reliability and effectiveness of Internet suicide-prevention tasks.


Asunto(s)
Medios de Comunicación Sociales , Suicidio , Humanos , Reproducibilidad de los Resultados , Inteligencia Artificial , Taiwán , Suicidio/prevención & control , Suicidio/psicología , Ideación Suicida , Internet
10.
Magn Reson Med ; 89(1): 40-53, 2023 01.
Artículo en Inglés | MEDLINE | ID: mdl-36161342

RESUMEN

PURPOSE: We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 ( 31 $$ {}^{31} $$ P) magnetic resonance spectra. The flexibility and speed of the technique rival traditional least-square fitting methods, with the performance of the two approaches, are compared in this work. THEORY AND METHODS: Convolutional neural network architectures have been proposed for the analysis and quantification of 31 $$ {}^{31} $$ P-spectroscopy. The generation of training and test data using a fully parameterized model is presented herein. In vivo unlocalized free induction decay and three-dimensional 31 $$ {}^{31} $$ P-magnetic resonance spectroscopy imaging data were acquired from healthy volunteers before being quantified using either 31P-SPAWNN or traditional least-square fitting techniques. RESULTS: The presented experiment has demonstrated both the reliability and accuracy of 31P-SPAWNN for estimating metabolite concentrations and spectral parameters. Simulated test data showed improved quantification using 31P-SPAWNN compared with LCModel. In vivo data analysis revealed higher accuracy at low signal-to-noise ratio using 31P-SPAWNN, yet with equivalent precision. Processing time using 31P-SPAWNN can be further shortened up to two orders of magnitude. CONCLUSION: The accuracy, reliability, and computational speed of the method open new perspectives for integrating these applications in a clinical setting.


Asunto(s)
Inteligencia Artificial , Fósforo , Humanos , Reproducibilidad de los Resultados , Espectroscopía de Resonancia Magnética/métodos , Redes Neurales de la Computación
11.
Methods Mol Biol ; 2597: 187-216, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-36374423

RESUMEN

Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.


Asunto(s)
Inteligencia Artificial , Péptidos , Péptidos/metabolismo , Proteínas/metabolismo , Programas Informáticos , Ligandos
12.
Curr Probl Diagn Radiol ; 52(1): 47-55, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-35618554

RESUMEN

With the rapid integration of artificial intelligence into medical practice, there has been an exponential increase in the number of scientific papers and industry players offering models designed for various tasks. Understanding these, however, is difficult for a radiologist in practice, given the core mathematical principles and complicated terminology involved. This review aims to elucidate the core mathematical concepts of both machine learning and deep learning models, explaining the various steps and common terminology in common layman language. Thus, by the end of this article, the reader should be able to understand the basics of how prediction models are built and trained, including challenges faced and how to avoid them. The reader would also be equipped to adequately evaluate various models, and take a decision on whether a model is likely to perform adequately in the real-world setting.


Asunto(s)
Algoritmos , Inteligencia Artificial , Humanos , Aprendizaje Automático , Radiólogos , Personal de Salud
13.
Carbohydr Polym ; 301(Pt A): 120300, 2023 Feb 01.
Artículo en Inglés | MEDLINE | ID: mdl-36436853

RESUMEN

Conductive hydrogels (CHs) have attracted considerable attentions in the fields of wearable electronics, disease diagnosis, and artificial intelligence. However, it is still a great challenge to prepare a single CH system with integrated characteristics of high stretchability, good transparency, and multisensory function through a simple fabrication process. Herein, carboxylic cellulose nanofibers (CCNF) were used to assist the homogeneous distribution of opaque conductive poly(3,4-ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT: PSS) into the crosslinked polyacrylamide network for the fabrication of stretchable and transparent interpenetrating network CH, aiming for a high-performance multisensory system. As expected, the ready formation of hydrogen bonds between the water molecules and a great deal of hydrophilic groups in the hydrogel endow the obtained CH with excellent humidity response behavior in a wide range (0-85%), and the introduction of CCNF and PEDOT: PSS is proved to be an effective strategy to enhance the humidity sensitivity, exhibiting great potential for the noncontact sensing of human respiration and finger movement. Meanwhile, it also displays excellent strain sensing behavior with favorable sensitivity in a broad range (0-837 %), fast response and reliable stability and reproducibility. Importantly, our prepared CH can also detect and discriminate complicated human activities and physiological signals. All these demonstrate the superiority of our prepared CH for the new generation of flexible wearable electronics.


Asunto(s)
Hidrogeles , Nanofibras , Humanos , Hidrogeles/química , Celulosa , Humedad , Reproducibilidad de los Resultados , Inteligencia Artificial
14.
Surv Ophthalmol ; 68(1): 42-53, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-35970233

RESUMEN

We review the application of artificial intelligence (AI) techniques in the screening, diagnosis, and classification of diabetic macular edema (DME) by searching six databases- PubMed, Scopus, Web of Science, Science Direct, IEEE, and ACM- from January 1, 2005 to July 4, 2021. A total of 879 articles were extracted, and by applying inclusion and exclusion criteria, 38 articles were selected for more evaluation. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We provide an overview of the current state of various AI techniques for DME screening, diagnosis, and classification using retinal imaging modalities such as optical coherence tomography (OCT) and color fundus photography (CFP). Based on our findings, deep learning models have an extraordinary capacity to provide an accurate and efficient system for DME screening and diagnosis. Using these in the processing of modalities leads to a significant increase in sensitivity and specificity values. The use of decision support systems and applications based on AI in processing retinal images provided by OCT and CFP increases the sensitivity and specificity in DME screening and detection.


Asunto(s)
Diabetes Mellitus , Retinopatía Diabética , Edema Macular , Humanos , Edema Macular/diagnóstico , Retinopatía Diabética/diagnóstico , Inteligencia Artificial , Tomografía de Coherencia Óptica/métodos , Retina
15.
Curr Probl Diagn Radiol ; 52(1): 1-5, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-36115775

RESUMEN

Given limited exposure to radiology during the pre-clinical and clinical years, it has been challenging to recruit medical students to radiology. Now, many medical students considering radiology as a career are deterred due to misinformation surrounding how AI implementation will affect radiologists in the future. Artificial Intelligence (AI) has the potential to revolutionize the way in which medicine is practiced, especially in the field of radiology, and will ultimately support radiologists and advance the specialty. We aimed to provide a basic guide for medical students on the application of artificial intelligence in radiology, address misconceptions, highlight the role radiologists will play in AI development, and discuss the challenges faced in the future.


Asunto(s)
Radiología , Estudiantes de Medicina , Humanos , Inteligencia Artificial , Radiólogos , Radiología/educación , Predicción
16.
Maturitas ; 167: 75-81, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-36308974

RESUMEN

Breast density (BD) and breast arterial calcifications (BAC) can expand the role of mammography. In premenopause, BD is related to body fat composition: breast adipose tissue and total volume are potential indicators of fat storage in visceral depots, associated with higher risk of cardiovascular disease (CVD). Women with fatty breast have an increased likelihood of hypercholesterolemia. Women without cardiometabolic diseases with higher BD have a lower risk of diabetes mellitus, hypertension, chest pain, and peripheral vascular disease, while those with lower BD are at increased risk of cardiometabolic diseases. BAC, the expression of Monckeberg sclerosis, are associated with CVD risk. Their prevalence, 13 % overall, rises after menopause and is reduced in women aged over 65 receiving hormonal replacement therapy. Due to their distinct pathogenesis, BAC are associated with hypertension but not with other cardiovascular risk factors. Women with BAC have an increased risk of acute myocardial infarction, ischemic stroke, and CVD death; furthermore, moderate to severe BAC load is associated with coronary artery disease. The clinical use of BAC assessment is limited by their time-consuming manual/visual quantification, an issue possibly solved by artificial intelligence-based approaches addressing BAC complex topology as well as their large spectrum of extent and x-ray attenuations. A link between BD, BAC, and osteoporosis has been reported, but data are still inconclusive. Systematic, standardised reporting of BD and BAC should be encouraged.


Asunto(s)
Enfermedades de la Mama , Hipertensión , Infarto del Miocardio , Femenino , Humanos , Inteligencia Artificial , Factores de Riesgo , Mamografía , Enfermedades de la Mama/diagnóstico por imagen , Enfermedades de la Mama/complicaciones , Enfermedades de la Mama/epidemiología , Hipertensión/complicaciones , Biomarcadores
17.
Sci Total Environ ; 856(Pt 2): 159283, 2023 Jan 15.
Artículo en Inglés | MEDLINE | ID: mdl-36208738

RESUMEN

Global food security, which has emerged as one of the sustainability challenges, impacts every country. As food cannot be generated without involving nutrients, research has intensified recently to recover unused nutrients from waste streams. As a finite resource, phosphorus (P) is largely wasted. This work critically reviews the technical applicability of various water technologies to recover macro-nutrients such as P, N, and K from wastewater. Struvite precipitation, adsorption, ion exchange, and membrane filtration are applied for nutrient recovery. Technological strengths and drawbacks in their applications are evaluated and compared. Their operational conditions such as pH, dose required, initial nutrient concentration, and treatment performance are presented. Cost-effectiveness of the technologies for P or N recovery is also elaborated. It is evident from a literature survey of 310 published studies (1985-2022) that no single technique can effectively and universally recover target macro-nutrients from liquid waste. Struvite precipitation is commonly used to recover over 95 % of P from sludge digestate with its concentration ranging from 200 to 4000 mg/L. The recovered precipitate can be reused as a fertilizer due to its high content of P and N. Phosphate removal of higher than 80 % can be achieved by struvite precipitation when the molar ratio of Mg2+/PO43- ranges between 1.1 and 1.3. The applications of artificial intelligence (AI) to collect data on critical parameters control optimization, improve treatment effectiveness, and facilitate water utilities to upscale water treatment plants. Such infrastructure in the plants could enable the recovered materials to be reused to sustain food security. As nutrient recovery is crucial in wastewater treatment, water treatment plant operators need to consider (1) the costs of nutrient recovery techniques; (2) their applicability; (3) their benefits and implications. It is essential to note that the treatment cost of P and/or N-laden wastewater depends on the process applied and local conditions.


Asunto(s)
Fertilizantes , Aguas Residuales , Estruvita/química , Aguas Residuales/química , Eliminación de Residuos Líquidos/métodos , Inteligencia Artificial , Fósforo/análisis , Fosfatos/química , Nutrientes , Minerales , Seguridad Alimentaria
18.
Surv Ophthalmol ; 68(1): 17-41, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-35985360

RESUMEN

Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma an examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is noninvasive and low-cost; however, image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is: "Can artificial intelligence mimic glaucoma assessments made by experts?" Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy? We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011 to 2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modeling-based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research.


Asunto(s)
Glaucoma , Disco Óptico , Humanos , Inteligencia Artificial , Fondo de Ojo , Glaucoma/diagnóstico , Disco Óptico/diagnóstico por imagen , Aprendizaje Automático
19.
J Mech Behav Biomed Mater ; 137: 105576, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-36413863

RESUMEN

The growing health and economic burden of bone fractures, their intricate multiscale features and the existing knowledge gaps in the comprehension of micro-scale bone damage occurrence make fracture diagnosis a challenging issue. In this scenario, deep-learning and artificial intelligence embody the new frontier of healthcare system, by overcoming the subjectivity of clinicians in the analysis of medical images. However, the preliminary attempts in exploiting the power of machine learning algorithms such as neural networks are still limited to bone macro-scale, while there is an evident lack in their application to smaller scales, where damage starts nucleating. Currently, speculations at the micro-scale are only feasible with the aid of high-resolution imaging techniques, that are particularly time consuming in terms of output images analysis. In this context, this works aims at combining the visualization of the micro-crack propagation mechanism with the promising application of convolutional neural networks. The implemented artificial intelligence tool is based for the first time on a large number of human synchrotron images coming from healthy and osteoporotic femoral heads tested under micro-compression. The designed convolutional neural networks are able to automatically detect lacunae and micro-cracks at different compression levels with high accuracy levels; indeed, with the baseline setup, networks achieve more than 0.99 level of accuracy for both cracks and lacunae, and more than 0.87 of the meanIoU adopted as validation metric. This approach is particularly encouraging for the development of powerful recognition system to comprehend bone micro-damage initiation and propagation, paving the way to the application of machine learning studies to bone micromechanics. This could be additionally crucial for future patient specific micro-scale observations to be related to the clinical practice.


Asunto(s)
Inteligencia Artificial , Sincrotrones , Humanos , Redes Neurales de la Computación , Aprendizaje Automático , Algoritmos
20.
Comput Math Methods Med ; 2022: 1217003, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35341007

RESUMEN

This research was aimed at investigating the artificial intelligence (AI) segmentation algorithm-based multislice spiral computed tomography (MSCT) in the diagnosis of liver cirrhosis and liver fibrosis. Besides, it was aimed at providing new methods for the diagnosis of liver cirrhosis and liver fibrosis. All patients were divided into the control group, mild liver fibrosis group, and significant liver fibrosis group. A total of 112 patients were included, with 40 cases in the mild liver fibrosis group, 48 cases in the significant liver fibrosis group, and 24 cases who underwent computed tomography (CT) examination in the control group. In the research, deconvolution algorithm of AI segmentation algorithm was adopted to process the images. The average hepatic arterial fraction (HAF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 17.59 ± 10.03%, 18.23 ± 5.57%, and 20.98 ± 6.63%, respectively. The average MTT values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 12.69 ± 1.78S, 12.53 ± 2.05S, and 12.04 ± 1.57S, respectively. The average blood flow (BF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 105.68 ± 15.57 mL 100 g-1·min-1, 116.07 ± 16.5 mL·100 g-1·min-1, and 110.39 ± 16.32 mL·100 g-1·min-1, respectively. Besides, the average blood volume (BV) values of patients in the control group, mild liver fibrosis group, and significant liver fibrosis group were 15.69 ± 4.35 mL·log-1, 16.97 ± 2.68 mL·log-1, and 16.11 ± 4.87 mL·100 g-1, respectively. According to statistics, the differences among the average HAF, MTT, BF, and BV values showed no statistical meaning. AI segmentation algorithm-based MSCT imaging could promote the diagnosis of liver cirrhosis and liver fibrosis effectively and offer new methods to clinical diagnosis of liver cirrhosis and liver fibrosis.


Asunto(s)
Inteligencia Artificial , Cirrosis Hepática , Algoritmos , Humanos , Cirrosis Hepática/diagnóstico por imagen , Tomografía Computarizada Espiral/métodos
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