Monitoring of fatigue in radiologists during prolonged image interpretation using fNIRS

Nishizaki, Takashi; Ishigaki, Takeo; Satake, Hiroko; Ito, Shinji; Kaii, O; Mori, Yasuki; Shimamoto, Kazuhiro; Fukushima, Hiromichi; Suzuki, Kôjiro; Umakoshi, Hironobu; Ohashi, Mitsuo; Kawaguchi, Fumio; Naganawa, Shinji

doi:10.1007/s11604-019-00826-2

Cited by 24 publications

(15 citation statements)

References 33 publications

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“…On the other hand, in the pandemic phase of COVID-19, requests for CT Exam has substantially increased. The increased workload can affect the diagnostic performance of radiologists [9,10]. The results of our study indicate that deep learning can characterize the pattern of abnormality effectively, and may help physicians in managing workflow and diagnosing infection related to the COVID-19.…”

Section: Discussionmentioning

confidence: 80%

“…Hence, further investigations are needed to help and improve the radiologist's performance. In addition, congestion of patients, as well as a high workload of radiologists, which can increase fatigue, affect diagnostic performance [9,10]. Although for the first time, Lodwick used the term computer-aided diagnosis (CAD) in the literature in 1966 [11], serious attempts on CAD began in the 1980s [12].…”

Section: Introductionmentioning

confidence: 99%

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Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ardakani

Kanafi

Acharya

et al. 2020

Computers in Biology and Medicine

768

567

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Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a wellequipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups:

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Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ardakani

Kanafi

Acharya

et al. 2020

Computers in Biology and Medicine

768

567

View full text Add to dashboard Cite

show abstract

“…FNIRS is not limited to the restrictive fMRI environment, and since it is relatively robust against motion artifacts, the technique allows for freely moving participants in contexts with high ecological validity and in the real world (or in situ) [1,2,12]. Examples of such studies include, but are not limited to, outdoor activities, such as riding a bike [13] and walking [14]; farm workers at individual farm locations [15]; driving a car on an expressway [16]; setting a table [17]; radiologists interpreting MRI and CT images [18]; exposure therapy of arachnophobia [19]; performing penalty kicks in soccer [20]; playing table tennis, playing the piano, and human interaction during a violin duo [12]. Such studies are important since they may help us understand how the brain functions in real-life situations.…”

Section: Introductionmentioning

confidence: 99%

Real-World fNIRS Brain Activity Measurements during Ashtanga Vinyasa Yoga

Dybvik

Steinert

2021

Brain Sciences

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Functional near-infrared spectroscopy (fNIRS) is often praised for its portability and robustness towards motion artifacts. While an increasing body of fNIRS research in real-world environments is emerging, most fNIRS studies are still conducted in laboratories, and do not incorporate larger movements performed by participants. This study extends fNIRS applications in real-world environments by conducting a single-subject observational study of a yoga practice with considerable movement (Ashtanga Vinyasa Yoga) in a participant’s natural environment (their apartment). The results show differences in cognitive load (prefrontal cortex activation) when comparing technically complex postures to relatively simple ones, but also some contrasts with surprisingly little difference. This study explores the boundaries of real-world cognitive load measurements, and contributes to the empirical knowledge base of using fNIRS in realistic settings. To the best of our knowledge, this is the first demonstration of fNIRS brain imaging recorded during any moving yoga practice. Future work with fNIRS should take advantage of this by accomplishing studies with considerable real-world movement.

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“…In this type of pandemic situation, a substantial increase in chest CT's is necessary. We require highly experienced radiologists, but there is a shortage of them [ 8 , 9 ]. This will help improve their performances by providing them with a computer-aided alternative.…”

Section: Introductionmentioning

confidence: 99%

Radiologist-Level Two Novel and Robust Automated Computer-Aided Prediction Models for Early Detection of COVID-19 Infection from Chest X-ray Images

et al. 2021

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COVID-19 is an ongoing pandemic that is widely spreading daily and reaches a significant community spread. X-ray images, computed tomography (CT) images and test kits (RT-PCR) are three easily available options for predicting this infection. Compared to the screening of COVID-19 infection from X-ray and CT images, the test kits(RT-PCR) available to diagnose COVID-19 face problems such as high analytical time, high false negative outcomes, poor sensitivity and specificity. Radiological signatures that X-rays can detect have been found in COVID-19 positive patients. Radiologists may examine these signatures, but it's a time-consuming and error-prone process (riddled with intra-observer variability). Thus, the chest X-ray analysis process needs to be automated, for which AI-driven tools have proven to be the best choice to increase accuracy and speed up analysis time, especially in the case of medical image analysis. We shortlisted four datasets and 20 CNN-based models to test and validate the best ones using 16 detailed experiments with fivefold cross-validation. The two proposed models, ensemble deep transfer learning CNN model and hybrid LSTMCNN, perform the best. The accuracy of ensemble CNN was up to 99.78% (96.51% average-wise), F1-score up to 0.9977 (0.9682 average-wise) and AUC up to 0.9978 (0.9583 average-wise). The accuracy of LSTMCNN was up to 98.66% (96.46% average-wise), F1-score up to 0.9974 (0.9668 average-wise) and AUC up to 0.9856 (0.9645 average-wise). These two best pre-trained transfer learning-based detection models can contribute clinically by offering the patients prediction correctly and rapidly.

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Monitoring of fatigue in radiologists during prolonged image interpretation using fNIRS

Cited by 24 publications

References 33 publications

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Real-World fNIRS Brain Activity Measurements during Ashtanga Vinyasa Yoga

Radiologist-Level Two Novel and Robust Automated Computer-Aided Prediction Models for Early Detection of COVID-19 Infection from Chest X-ray Images

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