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Surgery Surgery

Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery.

In World journal of gastroenterology ; h5-index 103.0

BACKGROUND : Colonic perfusion status can be assessed easily by indocyanine green (ICG) angiography to predict ischemia related anastomotic complications during laparoscopic colorectal surgery. Recently, various parameter-based perfusion analysis have been studied for quantitative evaluation, but the analysis results differ depending on the use of quantitative parameters due to differences in vascular anatomical structure. Therefore, it can help improve the accuracy and consistency by artificial intelligence (AI) based real-time analysis microperfusion (AIRAM).

AIM : To evaluate the feasibility of AIRAM to predict the risk of anastomotic complication in the patient with laparoscopic colorectal cancer surgery.

METHODS : The ICG curve was extracted from the region of interest (ROI) set in the ICG fluorescence video of the laparoscopic colorectal surgery. Pre-processing was performed to reduce AI performance degradation caused by external environment such as background, light source reflection, and camera shaking using MATLAB 2019 on an I7-8700k Intel central processing unit (CPU) PC. AI learning and evaluation were performed by dividing into a training patient group (n = 50) and a test patient group (n = 15). Training ICG curve data sets were classified and machine learned into 25 ICG curve patterns using a self-organizing map (SOM) network. The predictive reliability of anastomotic complications in a trained SOM network is verified using test set.

RESULTS : AI-based risk and the conventional quantitative parameters including T1/2max , time ratio (TR), and rising slope (RS) were consistent when colonic perfusion was favorable as steep increasing ICG curve pattern. When the ICG graph pattern showed stepped rise, the accuracy of conventional quantitative parameters decreased, but the AI-based classification maintained accuracy consistently. The receiver operating characteristic curves for conventional parameters and AI-based classification were comparable for predicting the anastomotic complication risks. Statistical performance verifications were improved in the AI-based analysis. AI analysis was evaluated as the most accurate parameter to predict the risk of anastomotic complications. The F1 score of the AI-based method increased by 31% for T1/2max , 8% for TR, and 8% for RS. The processing time of AIRAM was measured as 48.03 s, which was suitable for real-time processing.

CONCLUSION : In conclusion, AI-based real-time microcirculation analysis had more accurate and consistent performance than the conventional parameter-based method.

Park Sang-Ho, Park Hee-Min, Baek Kwang-Ryul, Ahn Hong-Min, Lee In Young, Son Gyung Mo

2020-Nov-28

Anastomotic complications, Artificial intelligent, Colorectal surgery, Indocyanine green, Laparoscopic, Microcirculation analysis

Internal Medicine Internal Medicine

Emerging use of artificial intelligence in inflammatory bowel disease.

In World journal of gastroenterology ; h5-index 103.0

Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations.

Kohli Arushi, Holzwanger Erik A, Levy Alexander N

2020-Nov-28

Artificial intelligence, Automated diagnostics, Colorectal neoplasia screening, Machine learning, Multiomic data, Predictive models

General General

Model-size reduction for reservoir computing by concatenating internal states through time.

In Scientific reports ; h5-index 158.0

Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called "reservoirs." To implement RC in edge computing, it is highly important to reduce the amount of computational resources that RC requires. In this study, we propose methods that reduce the size of the reservoir by inputting the past or drifting states of the reservoir to the output layer at the current time step. To elucidate the mechanism of model-size reduction, the proposed methods are analyzed based on information processing capacity proposed by Dambre et al. (Sci Rep 2:514, 2012). In addition, we evaluate the effectiveness of the proposed methods on time-series prediction tasks: the generalized Hénon-map and NARMA. On these tasks, we found that the proposed methods were able to reduce the size of the reservoir up to one tenth without a substantial increase in regression error.

Sakemi Yusuke, Morino Kai, Leleu Timothée, Aihara Kazuyuki

2020-Dec-11

General General

Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach.

In Scientific reports ; h5-index 158.0

Determination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system classification, except for triclinic cases, and with 88% accuracy for space group classification with five candidates. We also succeeded in quantifying empirical knowledge vaguely shared among experts, showing the possibility for data-driven discovery of unrecognised characteristics embedded in experimental data by using an interpretable ML approach.

Suzuki Yuta, Hino Hideitsu, Hawai Takafumi, Saito Kotaro, Kotsugi Masato, Ono Kanta

2020-Dec-11

Radiology Radiology

Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning.

In Scientific reports ; h5-index 158.0

In aneurysmal subarachnoid hemorrhage (aSAH), accurate diagnosis of aneurysm is essential for subsequent treatment to prevent rebleeding. However, aneurysm detection proves to be challenging and time-consuming. The purpose of this study was to develop and evaluate a deep learning model (DLM) to automatically detect and segment aneurysms in patients with aSAH on computed tomography angiography. In this retrospective single-center study, three different DLMs were trained on 68 patients with 79 aneurysms treated for aSAH (2016-2017) using five-fold-cross-validation. Their outputs were combined to a single DLM via ensemble-learning. The DLM was evaluated on an independent test set consisting of 185 patients with 215 aneurysms (2010-2015). Independent manual segmentations of aneurysms in a 3D voxel-wise manner by two readers (neurosurgeon, radiologist) provided the reference standard. For aneurysms > 30 mm3 (mean diameter of ~ 4 mm) on the test set, the DLM provided a detection sensitivity of 87% with false positives (FPs)/scan of 0.42. Automatic segmentations achieved a median dice similarity coefficient (DSC) of 0.80 compared to the reference standard. Aneurysm location (anterior vs. posterior circulation; P = .07) and bleeding severity (Fisher grade ≤ 3 vs. 4; P = .33) did not impede detection sensitivity or segmentation performance. For aneurysms > 100 mm3 (mean diameter of ~ 6 mm), a sensitivity of 96% with DSC of 0.87 and FPs/scan of 0.14 were obtained. In the present study, we demonstrate that the proposed DLM detects and segments aneurysms > 30 mm3 in patients with aSAH with high sensitivity independent of cerebral circulation and bleeding severity while producing FP findings of less than one per scan. Hence, the DLM can potentially assist treating physicians in aSAH by providing automated detection and segmentations of aneurysms.

Shahzad Rahil, Pennig Lenhard, Goertz Lukas, Thiele Frank, Kabbasch Christoph, Schlamann Marc, Krischek Boris, Maintz David, Perkuhn Michael, Borggrefe Jan

2020-Dec-11

General General

Automatic seizure detection based on imaged-EEG signals through fully convolutional networks.

In Scientific reports ; h5-index 158.0

Seizure detection is a routine process in epilepsy units requiring manual intervention of well-trained specialists. This process could be extensive, inefficient and time-consuming, especially for long term recordings. We proposed an automatic method to detect epileptic seizures using an imaged-EEG representation of brain signals. To accomplish this, we analyzed EEG signals from two different datasets: the CHB-MIT Scalp EEG database and the EPILEPSIAE project that includes scalp and intracranial recordings. We used fully convolutional neural networks to automatically detect seizures. For our best model, we reached average accuracy and specificity values of 99.3% and 99.6%, respectively, for the CHB-MIT dataset, and corresponding values of 98.0% and 98.3% for the EPILEPSIAE patients. For these patients, the inclusion of intracranial electrodes together with scalp ones increased the average accuracy and specificity values to 99.6% and 58.3%, respectively. Regarding the other metrics, our best model reached average precision of 62.7%, recall of 58.3%, F-measure of 59.0% and AP of 54.5% on the CHB-MIT recordings, and comparatively lowers performances for the EPILEPSIAE dataset. For both databases, the number of false alarms per hour reached values less than 0.5/h for 92% of the CHB-MIT patients and less than 1.0/h for 80% of the EPILEPSIAE patients. Compared to recent studies, our lightweight approach does not need any estimation of pre-selected features and demonstrates high performances with promising possibilities for the introduction of such automatic methods in the clinical practice.

Gómez Catalina, Arbeláez Pablo, Navarrete Miguel, Alvarado-Rojas Catalina, Le Van Quyen Michel, Valderrama Mario

2020-Dec-11