Use of an artificial intelligence-based rule extraction approach to predict an emergency cesarean section.

OBJECTIVE: One of the major problems with artificial intelligence (AI) is that it is generally known as a "black box". Therefore, the present study aimed to construct an emergency cesarean section (CS) prediction system using an AI-based rule extraction approach as a "white box" to detect the cause for the emergency CS. METHODS: Data were collected from all perinatal records of all delivery outcomes at Osaka Medical College between December 2014 and July 2019. We identified the delivery method for all deliveries after 36 gestational weeks as either (1) vaginal delivery or scheduled CS, or (2) emergency CS. From among these, we selected 52 risk factors to feed into an AI-based rule extraction algorithm to extract rules to predict an emergency CS. RESULTS: We identified 1513 singleton deliveries (1285 [84.9%] vaginal deliveries, 228 emergency CS [15.1%]) and extracted 15 rules. We achieved an average accuracy of 81.90% using five-fold cross-validation and an area under the receiving operating characteristic curve of 71.46%. CONCLUSION: To our knowledge, this is the first study to use interpretable AI-based rule extraction technology to predict an emergency CS. This system appears to be useful for identifying hidden factors for emergency CS.

Detection of Lower Albuminuria Levels and Early Development of Diabetic Kidney Disease Using an Artificial Intelligence-Based Rule Extraction Approach.

The aim of the present study was to determine the lowest cut-off value for albuminuria levels, which can be used to detect diabetic kidney disease (DKD) using the urinary albumin-to-creatinine ratio (UACR). National Health and Nutrition Examination Survey (NHANES) data for US adults were used, and participants were classified as having diabetes or prediabetes based on a self-report and physiological measures. The study dataset comprised 942 diabetes and 524 prediabetes samples. This study clarified the significance of the lower albuminuria (UACR) levels, which can detect DKD, using an artificial intelligence-based rule extraction approach. The diagnostic rules (15 concrete rules) for both samples were extracted using a recursive-rule eXtraction (Re-RX) algorithm with continuous attributes (continuous Re-RX) to discriminate between prediabetes and diabetes datasets. Continuous Re-RX showed high test accuracy (77.56%) and a large area under the receiver operating characteristics curve (75%), which derived the two cut-off values (6.1 mg/g Cr and 71.00 mg/g Cr) for the lower albuminuria level in the UACR to detect early development of DKD. The early cut-off values for normoalbuminuria (NA) and microalbuminuria (MA) will be determined to help detect CKD and DKD, and to detect diabetes before MA develop and to prevent diabetic complications.

The Right Direction Needed to Develop White-Box Deep Learning in Radiology, Pathology, and Ophthalmology: A Short Review.

The popularity of deep learning (DL) in the machine learning community has been dramatically increasing since 2012. The theoretical foundations of DL are well-rooted in the classical neural network (NN). Rule extraction is not a new concept, but was originally devised for a shallow NN. For about the past 30 years, extensive efforts have been made by many researchers to resolve the "black box" problem of trained shallow NNs using rule extraction technology. A rule extraction technology that is well-balanced between accuracy and interpretability has recently been proposed for shallow NNs as a promising means to address this black box problem. Recently, we have been confronting a "new black box" problem caused by highly complex deep NNs (DNNs) generated by DL. In this paper, we first review four rule extraction approaches to resolve the black box problem of DNNs trained by DL in computer vision. Next, we discuss the fundamental limitations and criticisms of current DL approaches in radiology, pathology, and ophthalmology from the black box point of view. We also review the conversion methods from DNNs to decision trees and point out their limitations. Furthermore, we describe a transparent approach for resolving the black box problem of DNNs trained by a deep belief network. Finally, we provide a brief description to realize the transparency of DNNs generated by a convolutional NN and discuss a practical way to realize the transparency of DL in radiology, pathology, and ophthalmology.

Use of a Deep Belief Network for Small High-Level Abstraction Data Sets Using Artificial Intelligence with Rule Extraction.

We describe a simple method to transfer from weights in deep neural networks (NNs) trained by a deep belief network (DBN) to weights in a backpropagation NN (BPNN) in the recursive-rule eXtraction (Re-RX) algorithm with J48graft (Re-RX with J48graft) and propose a new method to extract accurate and interpretable classification rules for rating category data sets. We apply this method to the Wisconsin Breast Cancer Data Set (WBCD), the Mammographic Mass Data Set, and the Dermatology Dataset, which are small, high-abstraction data sets with prior knowledge. After training these three data sets, our proposed rule extraction method was able to extract accurate and concise rules for deep NNs trained by a DBN. These results suggest that our proposed method could help fill the gap between the very high learning capability of DBNs and the very high interpretability of rule extraction algorithms such as Re-RX with J48graft.

Non-invasive prediction of non-alcoholic steatohepatitis in Japanese patients with morbid obesity by artificial intelligence using rule extraction technology.

AIM: To construct a non-invasive prediction algorithm for predicting non-alcoholic steatohepatitis (NASH), we investigated Japanese morbidly obese patients using artificial intelligence with rule extraction technology. METHODS: Consecutive patients who required bariatric surgery underwent a liver biopsy during the operation. Standard clinical, anthropometric, biochemical measurements were used as parameters to predict NASH and were analyzed using rule extraction technology. One hundred and two patients, including 79 NASH and 23 non-NASH patients were analyzed in order to create the prediction model, another cohort with 77 patients including 65 NASH and 12 non-NASH patients were analyzed to validate the algorithm. RESULTS: Alanine aminotransferase, C-reactive protein, homeostasis model assessment insulin resistance, albumin were extracted as predictors of NASH using a recursive-rule extraction algorithm. When we adopted the extracted rules for the validation cohort using a highly accurate rule extraction algorithm, the predictive accuracy was 79.2%. The positive predictive value, negative predictive value, sensitivity and specificity were 88.9%, 35.7%, 86.2% and 41.7%, respectively. CONCLUSION: We successfully generated a useful model for predicting NASH in Japanese morbidly obese patients based on their biochemical profile using a rule extraction algorithm.

Volumetric brain tumour detection from MRI using visual saliency.

Medical image processing has become a major player in the world of automatic tumour region detection and is tantamount to the incipient stages of computer aided design. Saliency detection is a crucial application of medical image processing, and serves in its potential aid to medical practitioners by making the affected area stand out in the foreground from the rest of the background image. The algorithm developed here is a new approach to the detection of saliency in a three dimensional multi channel MR image sequence for the glioblastoma multiforme (a form of malignant brain tumour). First we enhance the three channels, FLAIR (Fluid Attenuated Inversion Recovery), T2 and T1C (contrast enhanced with gadolinium) to generate a pseudo coloured RGB image. This is then converted to the CIE L*a*b* color space. Processing on cubes of sizes k = 4, 8, 16, the L*a*b* 3D image is then compressed into volumetric units; each representing the neighbourhood information of the surrounding 64 voxels for k = 4, 512 voxels for k = 8 and 4096 voxels for k = 16, respectively. The spatial distance of these voxels are then compared along the three major axes to generate the novel 3D saliency map of a 3D image, which unambiguously highlights the tumour region. The algorithm operates along the three major axes to maximise the computation efficiency while minimising loss of valuable 3D information. Thus the 3D multichannel MR image saliency detection algorithm is useful in generating a uniform and logistically correct 3D saliency map with pragmatic applicability in Computer Aided Detection (CADe). Assignment of uniform importance to all three axes proves to be an important factor in volumetric processing, which helps in noise reduction and reduces the possibility of compromising essential information. The effectiveness of the algorithm was evaluated over the BRATS MICCAI 2015 dataset having 274 glioma cases, consisting both of high grade and low grade GBM. The results were compared with that of the 2D saliency detection algorithm taken over the entire sequence of brain data. For all comparisons, the Area Under the receiver operator characteristic (ROC) Curve (AUC) has been found to be more than 0.99 ± 0.01 over various tumour types, structures and locations.

The number of microvascular complications is associated with an increased risk for severity of periodontitis in type 2 diabetes patients: Results of a multicenter hospital-based cross-sectional study.

AIMS/INTRODUCTION: To explore the relationships between periodontitis and microvascular complications as well as glycemic control in type 2 diabetes patients. MATERIALS AND METHODS: This multicenter, hospital-based, cross-sectional study included 620 patients with type 2 diabetes. We compared the prevalence and severity of periodontitis between patients with ≥1 microvascular complication and those without microvascular complications. We also compared the prevalence and severity of periodontitis among patients with different degrees of glycemic control. RESULTS: After adjusting for confounding factors, multiple logistic regression analysis showed that the severity of periodontitis was significantly associated with the number of microvascular complications (odds ratio 1.3, 95% confidence interval 1.1-1.6), glycated hemoglobin ≥8.0% (64 mmol/mol; odds ratio 1.6; 95% confidence interval 1.1-2.3), and older age (≥50 years; odds ratio 1.7; 95% confidence interval 1.1-2.6). However, the prevalence of periodontitis was not significantly associated with the number of microvascular complications, but was associated with male sex, high glycated hemoglobin (≥8.0% [64 mmol/mol]), older age (≥40 years), longer duration of diabetes (≥15 years) and fewer teeth (≤25). Furthermore, propensity score matching for age, sex, diabetes duration and glycated hemoglobin showed that the incidence of severe periodontitis was significantly higher among patients with microvascular complications than among those without microvascular complications (P < 0.05). CONCLUSIONS: The number of microvascular complications is a risk factor for more severe periodontitis in patients with type 2 diabetes, whereas poor glycemic control is a risk factor for increased prevalence and severity of periodontitis.

A Novel GBM Saliency Detection Model Using Multi-Channel MRI.

The automatic computerized detection of regions of interest (ROI) is an important step in the process of medical image processing and analysis. The reasons are many, and include an increasing amount of available medical imaging data, existence of inter-observer and inter-scanner variability, and to improve the accuracy in automatic detection in order to assist doctors in diagnosing faster and on time. A novel algorithm, based on visual saliency, is developed here for the identification of tumor regions from MR images of the brain. The GBM saliency detection model is designed by taking cue from the concept of visual saliency in natural scenes. A visually salient region is typically rare in an image, and contains highly discriminating information, with attention getting immediately focused upon it. Although color is typically considered as the most important feature in a bottom-up saliency detection model, we circumvent this issue in the inherently gray scale MR framework. We develop a novel pseudo-coloring scheme, based on the three MRI sequences, viz. FLAIR, T2 and T1C (contrast enhanced with Gadolinium). A bottom-up strategy, based on a new pseudo-color distance and spatial distance between image patches, is defined for highlighting the salient regions in the image. This multi-channel representation of the image and saliency detection model help in automatically and quickly isolating the tumor region, for subsequent delineation, as is necessary in medical diagnosis. The effectiveness of the proposed model is evaluated on MRI of 80 subjects from the BRATS database in terms of the saliency map values. Using ground truth of the tumor regions for both high- and low- grade gliomas, the results are compared with four highly referred saliency detection models from literature. In all cases the AUC scores from the ROC analysis are found to be more than 0.999 ± 0.001 over different tumor grades, sizes and positions.

Evaluation of effects of multiple candidate genes (LEP, LEPR, MC4R, PIK3C3, and VRTN) on production traits in Duroc pigs.

We evaluated multiple effects of genetic variations of five candidate loci (LEP, LEPR, MC4R, PIK3C3 and VRTN) on four production traits (average daily weight gain (ADG); backfat thickness (BFT); loin eye muscle area (EMA); and intramuscular fat content (IMF)) in a closed nucleus herd of pure Duroc pigs. Polymorphisms in LEPR, MC4R and PIK3C3 had significant single gene effects on ADG and BFT. The additive genetic variance in ADG and BFT (16.99% and 22.51%, respectively) was explained by genetic effects of these three loci. No correlations were observed between the LEP genotype and production traits in this study. Although we detected marginally epistatic interactions between LEPR and PIK3C3 on the eye muscle area, there were no significant epistatic effects on any traits among all loci pairs. These results suggest that LEPR, MC4R, PIK3C3 and VRTN may independently influence growth rate and fat deposition. Furthermore, the statistical models for predicting the breeding values of each trait had the lowest Akaike's information criterion values when considering the effect of the MC4R, LEPR, PIK3C3 and VRTN genotype simultaneously. These results suggest that LEPR, MC4R, PIK3C3 and VRTN are useful markers for accurately predicting breeding values in Duroc pigs.

Association of swine vertnin (VRTN) gene with production traits in Duroc pigs improved using a closed nucleus breeding system.

Vertnin (VRTN) is involved in the variation of vertebral number in pigs and it is located on Sus scrofa chromosome 7. Vertebral number is related to body size in pigs, and many reports have suggested presence of an association between body length (BL) and meat production traits. Therefore, we analyzed the relationship between the VRTN genotype and the production and body composition traits in purebred Duroc pigs. Intramuscular fat content (IMF) in the Longissimus muscle was significantly associated with the VRTN genotype. The mean IMF of individuals with the wild-type genotype (Wt/Wt) (5.22%) was greater than that of individuals with the Wt/Q (4.99%) and Q/Q genotypes (4.79%). In addition, a best linear unbiased predictor of multiple traits animal model showed that the Wt allele had a positive effect on the IMF breeding value. No associations were observed between the VRTN genotype and other production traits. The VRTN genotype was related to BL. The Q/Q genotype individuals (100.0 cm) were longer than individuals with the Wt/Q (99.5 cm) and Wt/Wt genotypes (98.9 cm). These results suggest that in addition to the maintenance of an appropriate backfat thickness value, VRTN has the potential to act as a genetic marker of IMF.

Association of an SNP marker in exon 24 of a class 3 phosphoinositide-3-kinase (PIK3C3) gene with production traits in Duroc pigs.

A C↔T single nucleotide polymorphism (SNP) on exon 24 of the porcine class 3 phosphoinositide-3-kinase (PIK3C3) gene is considered a possible genetic marker for selecting backfat (BF) thickness and carcass fat, although only one study has published results on its effects by performing experiments on a single resource family. We analyzed the association of this PIK3C3 polymorphism with production traits in 739 Duroc pigs. The C allele frequency was 67.9% in our study population. PIK3C3 polymorphism showed significant effects on average daily weight gain (ADG), BF thickness, intermuscular fat content (IMF), and the size of the loin eye muscle area (EMA). The C alleles increased ADG, BF and IMF, and decreased EMA. The predicted differences in traits between the homozygous pigs of the C and T alleles were 40 g/day for DG, 1.2 mm for BF, 0.44% for IMF, and 1.6 cm(2) for EMA. Furthermore, the statistical models for estimating the breeding values of each trait had lower Akaike's information criterion values when adding PIK3C3 genotype information. We therefore confirmed that the polymorphism in PIK3C3 (C2604T) has the potential to be a genetic marker for production traits in Duroc pigs.

Influence of acute aerobic exercise on adiponectin oligomer concentrations in middle-aged abdominally obese men.

Exercise intensity may induce changes in total adiponectin and adiponectin oligomer levels. However, the effects of acute aerobic exercise on total adiponectin and adiponectin oligomers in middle-aged abdominally obese men remain unknown. The purpose of this study was to investigate the influence of aerobic exercise intensity on changes in the concentrations of total adiponectin and adiponectin oligomers (high-molecular weight [HMW] and middle- plus low-molecular weight [MLMW] adiponectin), and the endocrine mechanisms involved in exercise-induced changes in adiponectin oligomer profiles in middle-aged abdominally obese men. Using a crossover design, 9 middle-aged abdominally obese men (age, 54.1 ± 2.4 years; body mass index, 27.9 ± 0.6 kg/m²) underwent 2 trials that consisted of 60 minutes of stationary cycle exercise at either moderate-intensity (ME) or high-intensity (HE) aerobic exercise (50% or 70% of peak oxygen uptake, respectively). Blood samples were collected to measure the concentrations of adiponectin oligomers, hormones (catecholamines, insulin, and growth hormone), metabolites (free fatty acid, glycerol, triglyceride, and glucose), and cytokines (interleukin-6 and tumor necrosis factor-α). After exercise, plasma catecholamine concentrations were higher during HE than during ME (P < .05). Total adiponectin concentration decreased at the end of HE (P < .05), but remained unchanged after ME. The HMW adiponectin concentration did not change at either intensity, whereas the MLMW concentration decreased at the end of HE (P < .05). The ratio of HMW to total adiponectin concentration increased significantly (P < .05), whereas the ratio of MLMW to total adiponectin concentration decreased significantly (P < .05), at the end of HE. The percentage changes in epinephrine concentration from baseline to the end of exercise were correlated with the percentage changes in total adiponectin concentration (r = -0.67, P < .05) and MLMW adiponectin concentration (r = -0.82, P < .05) from baseline to the end of HE. Our results indicate that the change in total adiponectin was mainly due to a change in MLMW adiponectin concentration during high-intensity exercise in middle-aged abdominally obese men. Epinephrine may partially regulate the decrease in total and MLMW adiponectin concentrations during high-intensity exercise.

Genetic networks and soft computing.

The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.

Sex differences in substrate oxidation during aerobic exercise in obese men and postmenopausal obese women.

The aim of this study was to compare substrate oxidation during aerobic exercise in obese men and postmenopausal obese women. Ten obese men (mean age, 55.4 +/- 2.2 years; body mass index, 27.5 +/- 0.4 kg/m(2); peak oxygen uptake [Vo(2)peak], 44.4 +/- 1.9 mL/kg fat-free mass/min; mean +/- SE] and 10 postmenopausal obese women (mean age, 57.2 +/- 1.2 years; body mass index, 27.9 +/- 0.5 kg/m(2); VO(2)peak, 39.9 +/- 1.3 mL/kg fat-free mass/min) performed a 40-minute bout of cycling exercise at 50% VO(2)peak. Blood samples were collected for assessment of metabolic variables and 17beta-estradiol concentration at baseline and during aerobic exercise. Breath samples were collected to estimate carbohydrate and fat oxidation using a digital computer-based breath-by-breath exercise analysis system during aerobic exercise. Serum 17beta-estradiol concentration was not significantly different between the men and women subjects at baseline (P > .05). Serum free fatty acid concentration tended to be higher in the men than in the women (P = .07) during the exercise, but the respiratory exchange ratio during exercise was lower in women than in men (P < .05). Fat oxidation adjusted for fat-free mass was higher (P < .05) in women than in men. These results suggest that fat utilization was higher during aerobic exercise in postmenopausal obese women than in obese men and did not depend on resting serum 17beta-estradiol concentration.

Acute effects of aerobic exercise on cognitive function in older adults.

The present study investigated the effects of acute aerobic exercise on cognitive brain functions of older adults. Twenty-four males (12 older and 12 younger adults) performed a modified flanker task during a baseline session (no exercise) and after light and moderate cycling exercise in counterbalanced order on different days while measures of task performance and the P3 component of an event-related brain potential were collected. The results indicated that, for both age groups, reaction time following moderate exercise was shorter relative to the other sessions, and P3 latencies following both light and moderate exercise were shorter compared with the baseline session. In contrast, P3 amplitude increased only following moderate exercise in younger adults. These findings suggest that light and moderate exercises improve cognitive function across the adult lifespan, although the mechanisms underlying the effects of observed acute aerobic exercise on cognitive function may be age dependent.

Greedy rule generation from discrete data and its use in neural network rule extraction.

This paper proposes a GRG (Greedy Rule Generation) algorithm, a new method for generating classification rules from a data set with discrete attributes. The algorithm is "greedy" in the sense that at every iteration, it searches for the best rule to generate. The criteria for the best rule include the number of samples and the size of subspaces that it covers, as well as the number of attributes in the rule. This method is employed for extracting rules from neural networks that have been trained and pruned for solving classification problems. The classification rules are extracted from the neural networks using the standard decompositional approach. Neural networks with one hidden layer are trained and the proposed GRG algorithm is applied to their discretized hidden unit activation values. Our experimental results show that neural network rule extraction with the GRG method produces rule sets that are accurate and concise. Application of GRG directly on three medical data sets with discrete attributes also demonstrates its effectiveness for rule generation.

Knowledge-based generation of diagnostic hypotheses and therapy recommendations for toxoplasma infections in pregnancy.

Primary infection of pregnant women with the parasite Toxoplasma gondii results in infections of the unborn by transplacental transmission in about 50% of the cases. The degree of possible damage depends on the duration of parasitical impact on fetal tissues. The web-based software system ToxoNet processes the results of serological antibody tests performed during pregnancy by means of a knowledge base containing medical knowledge on the interpretation of toxoplasmosis serology findings. For this purpose, it matches the results of all serological investigations of maternal blood with the content of the knowledge base and generates interpretive reports consisting of a diagnostic hypothesis, recommendations for therapy, and proposals for further investigations. Fuzzy sets are used to formalize certain intervals between subsequent investigations to take the varying immune responses of individual patients into account. In a retrospective study, ToxoNet classified 100% of the trivial serological cases and about 87.8% of the more complex cases correctly. ToxoNet comprises a knowledge base, a system for interpretation, and a knowledge acquisition and modification program. It is available on the WWW by accessing a medical knowledge-base server via standard browsers.

Plasma fat concentration increases in visceral fat obese men during high-intensity endurance exercise.

SUMMARY: The purpose of this study was to investigate differences in fat metabolism between visceral fat obese (VF-Ob) and abdominal subcutaneous obese (SF-Ob) men during "high-intensity endurance exercise". Fourteen obese (body mass index >25 kg/m(2)) men were classified into two groups according to visceral fat area using computed tomography; i.e., VF-Ob (n = 7; mean age, 52.0 ± 2.5 year) and SF-Ob (n = 7; mean age, 57.3 ± 2.8 year) groups. Plasma fat concentration and fat oxidation were measured at rest and during 60-min high-intensity (70% of peak oxygen uptake) stationary cycling exercise. Plasma concentrations of free fatty acid and glycerol were significantly higher (P ≤ 0.05) in VF-Ob men compared with SF-Ob men during endurance exercise. However, no significant difference was found in fat oxidation between VF-Ob and SF-Ob men (697 ± 135 and 661 ± 96 kJ/h, respectively) during high-intensity endurance exercise. These results suggest that obesity phenotype affects plasma fat concentration even during high-intensity exercise. It is likely that plasma fat concentrations in visceral fat obese men during high-intensity endurance exercise are more increased compared with during moderate-intensity endurance exercise. Despite the difference in plasma fat concentration, total fat oxidation was similar in the two obese phenotypes.:

Primitive neuroectodermal tumor arising in the colon: report of a case.

Peripheral primitive neuroectodermal tumors (pPNETs) are usually found in the soft tissue of the extremities, paravertebral region, and chest wall. We report a rare case of a pPNET arising in the colon. A 59-year-old man underwent left hemicolectomy for an infiltrative ulcerating tumor, 11 cm long, in the descending colon. Histological examination of the resected specimen revealed small, round cell proliferation with rosette-like structures, and confirmed regional lymph node involvement and peritoneal dissemination near the primary tumor. Immunohistochemically, the tumor cells were positive for synaptophysin and MIC2 (CD 99). ESW-FLI1 chimeric mRNA was detected in the tumor by reverse transcriptase-polymerase chain reaction. The patient underwent resection of recurrence in the retroperitoneum 3 months later, but metastasis rapidly developed and he died of the disease 7 months after his first operation.