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Radiology Radiology

AI co-pilot: content-based image retrieval for the reading of rare diseases in chest CT.

In Scientific reports ; h5-index 158.0

The aim of the study was to evaluate the impact of the newly developed Similar patient search (SPS) Web Service, which supports reading complex lung diseases in computed tomography (CT), on the diagnostic accuracy of residents. SPS is an image-based search engine for pre-diagnosed cases along with related clinical reference content ( https://eref.thieme.de ). The reference database was constructed using 13,658 annotated regions of interest (ROIs) from 621 patients, comprising 69 lung diseases. For validation, 50 CT scans were evaluated by five radiology residents without SPS, and three months later with SPS. The residents could give a maximum of three diagnoses per case. A maximum of 3 points was achieved if the correct diagnosis without any additional diagnoses was provided. The residents achieved an average score of 17.6 ± 5.0 points without SPS. By using SPS, the residents increased their score by 81.8% to 32.0 ± 9.5 points. The improvement of the score per case was highly significant (p = 0.0001). The residents required an average of 205.9 ± 350.6 s per case (21.9% increase) when SPS was used. However, in the second half of the cases, after the residents became more familiar with SPS, this increase dropped to 7%. Residents' average score in reading complex chest CT scans improved by 81.8% when the AI-driven SPS with integrated clinical reference content was used. The increase in time per case due to the use of the SPS was minimal.

Haubold Johannes, Zeng Ke, Farhand Sepehr, Stalke Sarah, Steinberg Hannah, Bos Denise, Meetschen Mathias, Kureishi Anisa, Zensen Sebastian, Goeser Tim, Maier Sandra, Forsting Michael, Nensa Felix

2023-Mar-16

Radiology Radiology

Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study.

In Insights into imaging

OBJECTIVES : To automatically segment prostate central gland (CG) and peripheral zone (PZ) on T2-weighted imaging using deep learning and assess the model's clinical utility by comparing it with a radiologist annotation and analyzing relevant influencing factors, especially the prostate zonal volume.

METHODS : A 3D U-Net-based model was trained with 223 patients from one institution and tested using one internal testing group (n = 93) and two external testing datasets, including one public dataset (ETDpub, n = 141) and one private dataset from two centers (ETDpri, n = 59). The Dice similarity coefficients (DSCs), 95th Hausdorff distance (95HD), and average boundary distance (ABD) were calculated to evaluate the model's performance and further compared with a junior radiologist's performance in ETDpub. To investigate factors influencing the model performance, patients' clinical characteristics, prostate morphology, and image parameters in ETDpri were collected and analyzed using beta regression.

RESULTS : The DSCs in the internal testing group, ETDpub, and ETDpri were 0.909, 0.889, and 0.869 for CG, and 0.844, 0.755, and 0.764 for PZ, respectively. The mean 95HD and ABD were less than 7.0 and 1.3 for both zones. The U-Net model outperformed the junior radiologist, having a higher DSC (0.769 vs. 0.706) and higher intraclass correlation coefficient for volume estimation in PZ (0.836 vs. 0.668). CG volume and Magnetic Resonance (MR) vendor were significant influencing factors for CG and PZ segmentation.

CONCLUSIONS : The 3D U-Net model showed good performance for CG and PZ auto-segmentation in all the testing groups and outperformed the junior radiologist for PZ segmentation. The model performance was susceptible to prostate morphology and MR scanner parameters.

Xu Lili, Zhang Gumuyang, Zhang Daming, Zhang Jiahui, Zhang Xiaoxiao, Bai Xin, Chen Li, Peng Qianyu, Jin Ru, Mao Li, Li Xiuli, Jin Zhengyu, Sun Hao

2023-Mar-16

Deep learning, Magnetic resonance imaging, Multicenter study, Prostate, Segmentation

General General

LST-EMG-Net: Long short-term transformer feature fusion network for sEMG gesture recognition.

In Frontiers in neurorobotics

With the development of signal analysis technology and artificial intelligence, surface electromyography (sEMG) signal gesture recognition is widely used in rehabilitation therapy, human-computer interaction, and other fields. Deep learning has gradually become the mainstream technology for gesture recognition. It is necessary to consider the characteristics of the surface EMG signal when constructing the deep learning model. The surface electromyography signal is an information carrier that can reflect neuromuscular activity. Under the same circumstances, a longer signal segment contains more information about muscle activity, and a shorter segment contains less information about muscle activity. Thus, signals with longer segments are suitable for recognizing gestures that mobilize complex muscle activity, and signals with shorter segments are suitable for recognizing gestures that mobilize simple muscle activity. However, current deep learning models usually extract features from single-length signal segments. This can easily cause a mismatch between the amount of information in the features and the information needed to recognize gestures, which is not conducive to improving the accuracy and stability of recognition. Therefore, in this article, we develop a long short-term transformer feature fusion network (referred to as LST-EMG-Net) that considers the differences in the timing lengths of EMG segments required for the recognition of different gestures. LST-EMG-Net imports multichannel sEMG datasets into a long short-term encoder. The encoder extracts the sEMG signals' long short-term features. Finally, we successfully fuse the features using a feature cross-attention module and output the gesture category. We evaluated LST-EMG-Net on multiple datasets based on sparse channels and high density. It reached 81.47, 88.24, and 98.95% accuracy on Ninapro DB2E2, DB5E3 partial gesture, and CapgMyo DB-c, respectively. Following the experiment, we demonstrated that LST-EMG-Net could increase the accuracy and stability of various gesture identification and recognition tasks better than existing networks.

Zhang Wenli, Zhao Tingsong, Zhang Jianyi, Wang Yufei

2023

gesture recognition, human-computer interaction, multi-head attention, multi-scale features, sEMG signals, stroke rehabilitation

General General

Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (Mpro) using molecular docking and deep learning methods.

In Computers in biology and medicine

Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-19 have yet to be developed to reduce mortality and transmission. Studies on the SARS-CoV-2 virus identified that its main protease (Mpro) might be a potential therapeutic target for drug development, as this enzyme plays a key role in viral replication. In search of potential inhibitors of Mpro, we developed a phytochemical library consisting of 2431 phytochemicals from 104 Korean medicinal plants that exhibited medicinal and antioxidant properties. The library was screened by molecular docking, followed by revalidation by re-screening with a deep learning method. Recurrent Neural Networks (RNN) computing system was used to develop an inhibitory predictive model using SARS coronavirus Mpro dataset. It was deployed to screen the top 12 compounds based on their docked binding affinity that ranged from -8.0 to -8.9 kcal/mol. The top two lead compounds, Catechin gallate and Quercetin 3-O-malonylglucoside, were selected depending on inhibitory potency against Mpro. Interactions with the target protein active sites, including His41, Met49, Cys145, Met165, and Thr190 were also examined. Molecular dynamics simulation was performed to analyze root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), solvent accessible surface area (SASA), and number of hydrogen bonds. Results confirmed the inflexible nature of the docked complexes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as bioactivity prediction confirmed the pharmaceutical activities of the lead compound. Findings of this research might help scientists to optimize compatible drugs for the treatment of COVID-19 patients.

Hossain Alomgir, Rahman Md Ekhtiar, Rahman Md Siddiqur, Nasirujjaman Khondokar, Matin Mohammad Nurul, Faruqe Md Omar, Rabbee Muhammad Fazle

2023-Mar-11

Catechin gallate, Deep learning, Main protease, Molecular docking, SARS-CoV-2

General General

Predicting heart failure onset in the general population using a novel data-mining artificial intelligence method.

In Scientific reports ; h5-index 158.0

We aimed to identify combinations of clinical factors that predict heart failure (HF) onset using a novel limitless-arity multiple-testing procedure (LAMP). We also determined if increases in numbers of predictive combinations of factors increases the probability of developing HF. We recruited people without HF who received health check-ups in 2010, who were followed annually for 4 years. Using 32,547 people, LAMP was performed to identify combinations of factors of fewer than four factors that could predict the onset of HF. The ability of the method to predict the probability of HF onset based on the number of matching predictive combinations of factors was determined in 275,658 people. We identified 549 combinations of factors for the onset of HF. Then we classified 275,658 people into six groups who had 0, 1-50, 51-100, 101-150, 151-200 or 201-250 predictive combinations of factors for the onset of HF. We found that the probability of HF progressively increased as the number of predictive combinations of factors increased. We identified combinations of variables that predict HF onset. An increased number of matching predictive combinations for the onset of HF increased the probability of HF onset.

Miyashita Yohei, Hitsumoto Tatsuro, Fukuda Hiroki, Kim Jiyoong, Washio Takashi, Kitakaze Masafumi

2023-Mar-16

General General

Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning.

In Scientific reports ; h5-index 158.0

The effectiveness, robustness, and flexibility of memory and learning constitute the very essence of human natural intelligence, cognition, and consciousness. However, currently accepted views on these subjects have, to date, been put forth without any basis on a true physical theory of how the brain communicates internally via its electrical signals. This lack of a solid theoretical framework has implications not only for our understanding of how the brain works, but also for wide range of computational models developed from the standard orthodox view of brain neuronal organization and brain network derived functioning based on the Hodgkin-Huxley ad-hoc circuit analogies that have produced a multitude of Artificial, Recurrent, Convolution, Spiking, etc., Neural Networks (ARCSe NNs) that have in turn led to the standard algorithms that form the basis of artificial intelligence (AI) and machine learning (ML) methods. Our hypothesis, based upon our recently developed physical model of weakly evanescent brain wave propagation (WETCOW) is that, contrary to the current orthodox model that brain neurons just integrate and fire under accompaniment of slow leaking, they can instead perform much more sophisticated tasks of efficient coherent synchronization/desynchronization guided by the collective influence of propagating nonlinear near critical brain waves, the waves that currently assumed to be nothing but inconsequential subthreshold noise. In this paper we highlight the learning and memory capabilities of our WETCOW framework and then apply it to the specific application of AI/ML and Neural Networks. We demonstrate that the learning inspired by these critically synchronized brain waves is shallow, yet its timing and accuracy outperforms deep ARCSe counterparts on standard test datasets. These results have implications for both our understanding of brain function and for the wide range of AI/ML applications.

Galinsky Vitaly L, Frank Lawrence R

2023-Mar-16