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1.
Ergonomics ; : 1-19, 2024 Sep 16.
Artigo em Inglês | MEDLINE | ID: mdl-39285689

RESUMO

The origins of Human Factors (HF) are rooted in the Second World War. It is a sign of the times that 75 years on from the formation of the Ergonomics Research Society, discussions occur as to whether Artificial Intelligence (AI) could/should be capable of controlling weaponry in a theatre of war. HF can support the design of safe, ethical, and usable AI: but there is little evidence of HF influencing industrial organisations developing AI. A review of the history of HF was conducted to understand how the influence of discipline on AI development may be optimised. The field may need to become broader and more inclusive, given the potential implications of innovation such as AI. The field of Responsible Research and Innovation can help the HF Practitioner ensure that the design and application of AI based technology serves to improve human well-being and optimise system performance over the next 75 years.Practitioner summary: A review of the history and origins of Human Factors was conducted. The review aimed to learn from the development of the discipline over the last 75 years to provide insights of what can be done to optimise the influence of HF to design safe, ethical, and usable artificial intelligence.

2.
Int J Comput Assist Radiol Surg ; 19(8): 1589-1596, 2024 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-38758290

RESUMO

PURPOSE: Body composition measurements from routine abdominal CT can yield personalized risk assessments for asymptomatic and diseased patients. In particular, attenuation and volume measures of muscle and fat are associated with important clinical outcomes, such as cardiovascular events, fractures, and death. This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool. METHODS: We assessed the tools across 900 CT series from the publicly available SAROS dataset, focusing on muscle, subcutaneous fat, and visceral fat. The Dice score was employed to assess accuracy in subcutaneous fat and muscle segmentation. Due to the lack of ground truth segmentations for visceral fat, Cohen's Kappa was utilized to assess segmentation agreement between the tools. RESULTS: Our Internal tool achieved a 3% higher Dice (83.8 vs. 80.8) for subcutaneous fat and a 5% improvement (87.6 vs. 83.2) for muscle segmentation, respectively. A Wilcoxon signed-rank test revealed that our results were statistically different with p < 0.01. For visceral fat, the Cohen's Kappa score of 0.856 indicated near-perfect agreement between the two tools. Our internal tool also showed very strong correlations for muscle volume (R 2 =0.99), muscle attenuation (R 2 =0.93), and subcutaneous fat volume (R 2 =0.99) with a moderate correlation for subcutaneous fat attenuation (R 2 =0.45). CONCLUSION: Our findings indicated that our Internal tool outperformed TotalSegmentator in measuring subcutaneous fat and muscle. The high Cohen's Kappa score for visceral fat suggests a reliable level of agreement between the two tools. These results demonstrate the potential of our tool in advancing the accuracy of body composition analysis.


Assuntos
Composição Corporal , Tomografia Computadorizada por Raios X , Humanos , Tomografia Computadorizada por Raios X/métodos , Composição Corporal/fisiologia , Reprodutibilidade dos Testes , Masculino , Gordura Intra-Abdominal/diagnóstico por imagem , Feminino , Gordura Subcutânea/diagnóstico por imagem , Músculo Esquelético/diagnóstico por imagem , Pessoa de Meia-Idade , Idoso
3.
ArXiv ; 2024 Mar 06.
Artigo em Inglês | MEDLINE | ID: mdl-38711428

RESUMO

Accurate training labels are a key component for multi-class medical image segmentation. Their annotation is costly and time-consuming because it requires domain expertise. In our previous work, a dual-branch network was developed to segment single-class edematous adipose tissue. Its inputs include a few strong labels from manual annotation and many inaccurate weak labels from existing segmentation methods. The dual-branch network consists of a shared encoder and two decoders to process weak and strong labels. Self-supervision iteratively updates weak labels during the training process. This work aims to follow this strategy and automatically improve training labels for multi-class image segmentation. Instead of using weak and strong labels to only train the network once in the previous work, transfer learning is used to train the network and improve weak labels sequentially. The dual-branch network is first trained by weak labels alone to initialize model parameters. After the network is stabilized, the shared encoder is frozen, and strong and weak decoders are fine-tuned by strong and weak labels together. The accuracy of weak labels is iteratively improved in the fine-tuning process. The proposed method was applied to a three-class segmentation of muscle, subcutaneous and visceral adipose tissue on abdominal CT scans. Validation results on 11 patients showed that the accuracy of training labels was statistically significantly improved, with the Dice similarity coefficient of muscle, subcutaneous and visceral adipose tissue increased from 74.2% to 91.5%, 91.2% to 95.6%, and 77.6% to 88.5%, respectively (p<0.05). In comparison with our earlier method, the label accuracy was also significantly improved (p<0.05). These experimental results suggested that the combination of the dual-branch network and transfer learning is an efficient means to improve training labels for multi-class segmentation.

4.
ArXiv ; 2024 Apr 12.
Artigo em Inglês | MEDLINE | ID: mdl-38410656

RESUMO

Purpose: Body composition measurements from routine abdominal CT can yield personalized risk assessments for asymptomatic and diseased patients. In particular, attenuation and volume measures of muscle and fat are associated with important clinical outcomes, such as cardiovascular events, fractures, and death. This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool. Methods: We assessed the tools across 900 CT series from the publicly available SAROS dataset, focusing on muscle, subcutaneous fat, and visceral fat. The Dice score was employed to assess accuracy in subcutaneous fat and muscle segmentation. Due to the lack of ground truth segmentations for visceral fat, Cohen's Kappa was utilized to assess segmentation agreement between the tools. Results: Our Internal tool achieved a 3% higher Dice (83.8 vs. 80.8) for subcutaneous fat and a 5% improvement (87.6 vs. 83.2) for muscle segmentation respectively. A Wilcoxon signed-rank test revealed that our results were statistically different with p < 0.01. For visceral fat, the Cohen's kappa score of 0.856 indicated near-perfect agreement between the two tools. Our internal tool also showed very strong correlations for muscle volume (R2=0.99), muscle attenuation (R2=0.93), and subcutaneous fat volume (R2=0.99) with a moderate correlation for subcutaneous fat attenuation (R2=0.45). Conclusion: Our findings indicated that our Internal tool outperformed TotalSegmentator in measuring subcutaneous fat and muscle. The high Cohen's Kappa score for visceral fat suggests a reliable level of agreement between the two tools. These results demonstrate the potential of our tool in advancing the accuracy of body composition analysis.

5.
Int J Comput Assist Radiol Surg ; 19(3): 443-448, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38233598

RESUMO

PURPOSE: Edema, or swelling, is a common symptom of kidney, heart, and liver disease. Volumetric edema measurement is potentially clinically useful. Edema can occur in various tissues. This work focuses on segmentation and volume measurement of one common site, subcutaneous adipose tissue. METHODS: The density distributions of edema and subcutaneous adipose tissue are represented as a two-class Gaussian mixture model (GMM). In previous work, edema regions were segmented by selecting voxels with density values within the edema density distribution. This work improves upon the prior work by generating an adipose tissue mask without edema through a conditional generative adversarial network. The density distribution of the generated mask was imported into a Chan-Vese level set framework. Edema and subcutaneous adipose tissue are separated by iteratively updating their respective density distributions. RESULTS: Validation results on 25 patients with edema showed that the segmentation accuracy significantly improved. Compared to GMM, the average Dice Similarity Coefficient increased from 56.0 to 61.7% ([Formula: see text]) and the relative volume difference decreased from 36.5 to 30.2% ([Formula: see text]). CONCLUSION: The generated adipose tissue density prior improved edema segmentation accuracy. Accurate edema volume measurement may prove clinically useful.


Assuntos
Abdome , Insuficiência Cardíaca , Humanos , Edema/diagnóstico por imagem , Tecido Adiposo/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Processamento de Imagem Assistida por Computador/métodos
6.
Cells ; 8(3)2019 03 16.
Artigo em Inglês | MEDLINE | ID: mdl-30884855

RESUMO

Small GTPases are a family of low molecular weight GTP-hydrolyzing enzymes that cycle between an inactive state when bound to GDP and an active state when associated to GTP. Small GTPases regulate key cellular processes (e.g., cell differentiation, proliferation, and motility) as well as subcellular events (e.g., vesicle trafficking), making them key participants in a great array of pathophysiological processes. Indeed, the dysfunction and deregulation of certain small GTPases, such as the members of the Ras and Arf subfamilies, have been related with the promotion and progression of cancer. Therefore, the development of inhibitors that target dysfunctional small GTPases could represent a potential therapeutic strategy for cancer treatment. This review covers the basic biochemical mechanisms and the diverse functions of small GTPases in cancer. We also discuss the strategies and challenges of inhibiting the activity of these enzymes and delve into new approaches that offer opportunities to target them in cancer therapy.


Assuntos
Proteínas Monoméricas de Ligação ao GTP/metabolismo , Neoplasias/tratamento farmacológico , Neoplasias/enzimologia , Transdução de Sinais , Animais , Inibidores Enzimáticos/uso terapêutico , Humanos , Terapia de Alvo Molecular
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