scholarly journals Implementation and Application of an Intelligent Pterygium Diagnosis System Based on Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Wei Xu ◽  
Ling Jin ◽  
Peng-Zhi Zhu ◽  
Kai He ◽  
Wei-Hua Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Anterior Segment ◽  
Diagnostic System ◽  
Diagnostic Model ◽  
Diagnosis System ◽  
Kappa Value ◽  
Group 2 ◽  
Sensitivity Specificity ◽  
Group 1

Objective: This study aims to implement and investigate the application of a special intelligent diagnostic system based on deep learning in the diagnosis of pterygium using anterior segment photographs.Methods: A total of 1,220 anterior segment photographs of normal eyes and pterygium patients were collected for training (using 750 images) and testing (using 470 images) to develop an intelligent pterygium diagnostic model. The images were classified into three categories by the experts and the intelligent pterygium diagnosis system: (i) the normal group, (ii) the observation group of pterygium, and (iii) the operation group of pterygium. The intelligent diagnostic results were compared with those of the expert diagnosis. Indicators including accuracy, sensitivity, specificity, kappa value, the area under the receiver operating characteristic curve (AUC), as well as 95% confidence interval (CI) and F1-score were evaluated.Results: The accuracy rate of the intelligent diagnosis system on the 470 testing photographs was 94.68%; the diagnostic consistency was high; the kappa values of the three groups were all above 85%. Additionally, the AUC values approached 100% in group 1 and 95% in the other two groups. The best results generated from the proposed system for sensitivity, specificity, and F1-scores were 100, 99.64, and 99.74% in group 1; 90.06, 97.32, and 92.49% in group 2; and 92.73, 95.56, and 89.47% in group 3, respectively.Conclusion: The intelligent pterygium diagnosis system based on deep learning can not only judge the presence of pterygium but also classify the severity of pterygium. This study is expected to provide a new screening tool for pterygium and benefit patients from areas lacking medical resources.

A Comparative Study Between Preoperative Subconjunctival Bevacizumab Injection And Postoperative Topical Application In Prevention Of Recurrence After Excision Of Primary Pterygium In Egyptian Patients

QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Basma Helal Mohamed ◽  
Othman Ali Othman Ziko ◽  
Hisham M Khairy Abd El Dayem ◽  
Nancy Ezzelregal Khamis Ahmed
Keyword(s):  
Corneal Thickness ◽  
Anterior Segment ◽  
P Value ◽  
Eye Drops ◽  
Subconjunctival Injection ◽  
Fibrovascular Tissue ◽  
Significant Difference ◽  
Group 2 ◽  
Group 1 ◽  
Primary Pterygium

Abstract Purpose to compare between recurrence incidence after primary pterygium excision when using preoperative subconjunctival injection of Bevacizumab (Avastin) and using it as a postoperative eye drops. Methods thirty two eyes of thirty patients (two patients had bilateral pterygium) with primary pterygia were clinically examined, classified into 3 groups and operated by simple excision with bare sclera technique. Group 1 included 10 patients received Bevacizumab (Avastin) in the form of eye drops (10 mg/ml) 3 times daily for 6 days postoperative. Group 2 included 10 patients received preoperative Bevacizumab in the form of subconjunctival injection (1.25 mg/0.05ml) single dose 1 week preoperative. Group 3 included 10 patients (12 eyes) 2 patients with bilateral Pterygium didn’t receive any form of Bevacizumab. Postoperative follow up was done clinically and by serial photography at 1 week, 1 month, 3 months and 6 months searching for signs of recurrence and/or complications. Results The results showed different grades of recurrence in 18 eyes of 32.True recurrence was seen in 7 patients of 18 (1 patient in group 1, 2 in group 2 and 4 in group3).Recurrence grades in group 1and 2 who used the Bevacizumab (20%grade II, 50% grade III, and 30% grade IV). Recurrence could be predicted by 100% depending on fibrovascular tissue appearing in the surgical bed at 3 months postoperative (P value 0.038).Preoperative fleshy pterygium has high statistical significance in realation to recurrence(P value = 0.006).Patient’s sex, residence and occupation had no statistically significant value in the process of recurrence (P value > 0.05). Patients with recurrent Pterygia (in group 1&2) had statistically significant changes in the corneal K- readings at 3 months and 6 months.No significant difference in the limbal or central corneal thickness in the operated eye and the other eye (Pvalue > 0.05). Conclusion Bevacizumab (Avastin) is a well tolerated drug with multiple drug delivery methods.The eye drops give better results than the subconjunctival injection.Appearance of fibrovascular tissue in the surgical bed at 3 months predict the recurrence by 100%. Preoperative fleshy pterygia will mostly recur again whatever Bevacizumab form was used .The corneal thickness by anterior segment OCT has no role in prediction or detection of early pterygium recurrence.

scholarly journals Patient-level detection of grade group ≥2 prostate cancer using quantitative diffusion MRI

2021 ◽  
Author(s):  
Allison Y Zhong ◽  
Leonardino A Digma ◽  
Troy Hussain ◽  
Christine H Feng ◽  
Christopher C Conlin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Characteristic Curve ◽  
Grade Group ◽  
Bootstrap Confidence Intervals ◽  
Patient Level ◽  
Apparent Diffusion ◽  
Spectrum Imaging ◽  
Group 2 ◽  
Clinically Significant ◽  
Group 1

Purpose: Multiparametric MRI (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the qualitative PI-RADS system and quantitative apparent diffusion coefficient (ADC) yield inconsistent results. An advanced Restrictrion Spectrum Imaging (RSI) model may yield a better quantitative marker for csPCa, the RSI restriction score (RSIrs). We evaluated RSIrs for patient-level detection of csPCa. Materials and Methods: Retrospective analysis of men who underwent mpMRI with RSI and prostate biopsy for suspected prostate cancer from 2017-2019. Maximum RSIrs within the prostate was assessed by area under the receiver operating characteristic curve (AUC) for discriminating csPCa (grade group ≥2) from benign or grade group 1 biopsies. Performance of RSIrs was compared to minimum ADC and PI-RADS v2-2.1via bootstrap confidence intervals and bootstrap difference (two-tailed α=0.05). We also tested whether the combination of PI-RADS and RSIrs (PI-RADS+RSIrs) was superior to PI-RADS, alone. Results: 151 patients met criteria for inclusion. AUC values for ADC, RSIrs, and PI-RADS were 0.50 [95% confidence interval: 0.41, 0.60], 0.76 [0.68, 0.84], and 0.78 [0.71, 0.85], respectively. RSIrs (p=0.0002) and PI-RADS (p<0.0001) were superior to ADC for patient-level detection of csPCa. The performance of RSIrs was comparable to that of PI-RADS (p=0.6). AUC for PI-RADS+RSIrs was 0.84 [0.77, 0.90], superior to PI-RADS or RSIrs, alone (p=0.008, p=0.009). Conclusions: RSIrs was superior to conventional ADC and comparable to (routine, clinical) PI-RADS for patient-level detection of csPCa. The combination of PI-RADS and RSIrs was superior to either alone. RSIrs is a promising quantitative marker worthy of prospective study in the setting of csPCa detection.

Using convolutional neural network and long short time memory to automatically detect aneurysm on 2D DSA images (Preprint)

2021 ◽  
Author(s):  
JunHua Liao ◽  
LunXin Liu ◽  
HaiHan Duan ◽  
YunZhi Huang ◽  
LiangXue Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intracranial Aneurysm ◽  
Spatial Information ◽  
Diagnostic System ◽  
Two Stage ◽  
Regional Localization ◽  
Detection Stage ◽  
Aneurysm Detection ◽  
Sensitivity Specificity ◽  
Computation Load

BACKGROUND It is hard to distinguish cerebral aneurysm from overlap vessels based on the 2D DSA images, for its lack the spatial information. OBJECTIVE The aim of this study is to construct a deep learning diagnostic system to improve the ability of detecting the PCoA aneurysm on 2D-DSA images and validate the efficiency of deep learning diagnostic system in 2D-DSA aneurysm detecting. METHODS We proposed a two stage detecting system. First, we established the regional localization stage (RLS) to automatically locate specific detection region of raw 2D-DSA sequences. And then, in the intracranial aneurysm detection stage (IADS) ,we build three different frames, RetinaNet, RetinaNet+LSTM, Bi-input+RetinaNet+LSTM, to detect the aneurysms. Each of the frame had fivefold cross-validation scheme. The area under curve (AUC), the receiver operating characteristic (ROC) curve, and mean average precision (mAP) were used to validate the efficiency of different frames. The sensitivity, specificity and accuracy were used to identify the ability of different frames. RESULTS 255 patients with PCoA aneurysms and 20 patients without aneurysm were included in this study. The best results of AUC of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 0.95, 0.96, and 0.97, respectively. The sensitivity of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 81.65% (59.40% to 94.76%), 87.91% (64.24% to 98.27%), 84.50% (69.57% to 93.97%), respectively. The specificity of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 88.89% (66.73% to 98.41%), 88.12% (66.06% to 98.08%), and 88.50% (74.44% to 96.39%), respectively. The accuracy of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 92.71% (71.29% to 99.54%), 89.42% (68.13% to 98.49%), and 91.00% (77.63% to 97.72%), respectively. CONCLUSIONS Two stage aneurysm detecting system can reduce time cost and the computation load. According to our results, more spatial and temporal information can help improve the performance of the frames, so that Bi-input+RetinaNet+LSTM has the best performance compared to other frames. And our study can demonstrate that our system was feasible to assist doctor to detect intracranial aneurysm on 2D-DSA images.

scholarly journals The Integration of qSOFA with Clinical Variables and Serum Biomarkers Improves the Prognostic Value of qSOFA Alone in Patients with Suspected or Confirmed Sepsis at ED Admission

2020 ◽  
Vol 9 (4) ◽  
pp. 1205 ◽  
Author(s):  
Filippo Mearelli ◽  
Giulia Barbati ◽  
Chiara Casarsa ◽  
Carlo Giansante ◽  
Andrea Breglia ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Characteristic Curve ◽  
Serum Biomarkers ◽  
Ease Of Use ◽  
Prognostic Models ◽  
Sepsis Group ◽  
Clinical Variables ◽  
Risk Of Death ◽  
Group 2 ◽  
Group 1

Background: The prognostic value of quick sepsis-related organ failure assessment (qSOFA) outside intensive care units has been criticized. Therefore, we aimed to improve its ability in predicting 30-day all-cause mortality, and in ruling out the cases at high risk of death among patients with suspected or confirmed sepsis at emergency department (ED) admission. Methods: This study is a secondary analysis of a prospective multicenter study. We built three predictive models combining qSOFA with the clinical variables and serum biomarkers that resulted in an independent association with 30-day mortality, in both 848 undifferentiated patients (Group 1) and in 545 patients definitively diagnosed with sepsis (Group 2). The models reaching the highest negative predictive value (NPV) with the minimum expenditure of biomarkers in Group 1 and in Group 2 were validated in two cohorts of patients initially held out due to missing data. Results: In terms of the area under the receiver-operating characteristic curve, all six models significantly exceeded qSOFA in predicting prognosis. An “extended” qSOFA (eqSOFA1) in Group 1 and an eqSOFA2 integrated with C-reactive protein and mid-regional proadrenomedullin (eqSOFA2+CRP+MR-proADM) in Group 2 reached the best NPV (0.94 and 0.93, respectively) and ease of use. eqSOFA1 and eqSOFA2+CRP+MR-proADM performed equally well in both the inception and validation cohorts. Conclusions: We have derived and validated two prognostic models that outweigh qSOFA in predicting mortality and in identifying the low risk of death among patients with suspected or confirmed sepsis at ED admission.

scholarly journals Can Deep Learning-Based Volumetric Analysis Predict Oxygen Demand Increase in Patients with COVID-19 Pneumonia?

Medicina ◽  
2021 ◽  
Vol 57 (11) ◽  
pp. 1148
Author(s):  
Marie Takahashi ◽  
Tomoyuki Fujioka ◽  
Toshihiro Horii ◽  
Koichiro Kimura ◽  
Mizuki Kimura ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Oxygen Demand ◽  
Volumetric Analysis ◽  
Ct Scans ◽  
Image Analysis System ◽  
Volume Data ◽  
Respiratory Status ◽  
Analysis System ◽  
Sensitivity Specificity

Background and Objectives: This study aimed to investigate whether predictive indicators for the deterioration of respiratory status can be derived from the deep learning data analysis of initial chest computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19). Materials and Methods: Out of 117 CT scans of 75 patients with COVID-19 admitted to our hospital between April and June 2020, we retrospectively analyzed 79 CT scans that had a definite time of onset and were performed prior to any medication intervention. Patients were grouped according to the presence or absence of increased oxygen demand after CT scan. Quantitative volume data of lung opacity were measured automatically using a deep learning-based image analysis system. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the opacity volume data were calculated to evaluate the accuracy of the system in predicting the deterioration of respiratory status. Results: All 79 CT scans were included (median age, 62 years (interquartile range, 46–77 years); 56 (70.9%) were male. The volume of opacity was significantly higher for the increased oxygen demand group than for the nonincreased oxygen demand group (585.3 vs. 132.8 mL, p < 0.001). The sensitivity, specificity, and AUC were 76.5%, 68.2%, and 0.737, respectively, in the prediction of increased oxygen demand. Conclusion: Deep learning-based quantitative analysis of the affected lung volume in the initial CT scans of patients with COVID-19 can predict the deterioration of respiratory status to improve treatment and resource management.

scholarly journals Deep Learning-Based CT Imaging in Diagnosing Myeloma and Its Prognosis Evaluation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jinzhou Wang ◽  
Xiangjun Shi ◽  
Xingchen Yao ◽  
Jie Ren ◽  
Xinru Du
Keyword(s):  
Deep Learning ◽  
Plasma Cells ◽  
Ct Images ◽  
Erector Spinae ◽  
Detection Accuracy ◽  
Research Subjects ◽  
Before And After ◽  
Group 2 ◽  
Psoas Major ◽  
Group 1

Imaging examination plays an important role in the early diagnosis of myeloma. The study focused on the segmentation effects of deep learning-based models on CT images for myeloma, and the influence of different chemotherapy treatments on the prognosis of patients. Specifically, 186 patients with suspected myeloma were the research subjects. The U-Net model was adjusted to segment the CT images, and then, the Faster region convolutional neural network (RCNN) model was used to label the lesions. Patients were divided into bortezomib group (group 1, n = 128) and non-bortezomib group (group 2, n = 58). The biochemical indexes, blood routine indexes, and skeletal muscle of the two groups were compared before and after chemotherapy. The results showed that the improved U-Net model demonstrated good segmentation results, the Faster RCNN model can realize the labeling of the lesion area in the CT image, and the classification accuracy rate was as high as 99%. Compared with group 1, group 2 showed enlarged psoas major and erector spinae muscle after treatment and decreased bone marrow plasma cells content, blood M protein, urine 24 h light chain, pBNP, ß-2 microglobulin (β2MG), ALP, and white blood cell (WBC) levels ( P < 0.05 ). In conclusion, deep learning is suggested in the segmentation and classification of CT images for myeloma, which can lift the detection accuracy. Two different chemotherapy regimens both improve the prognosis of patients, but the effects of non-bortezomib chemotherapy are better.

scholarly journals Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bo Zheng ◽  
Yunfang Liu ◽  
Kai He ◽  
Maonian Wu ◽  
Ling Jin ◽  
...  
Keyword(s):  
Fibrous Tissue ◽  
Anterior Segment ◽  
Clinical Results ◽  
Time Curve ◽  
Model Based ◽  
Study Results ◽  
Image Diagnosis ◽  
Kappa Value ◽  
Diagnosis Model ◽  
Sensitivity Specificity

Aims. The lack of primary ophthalmologists in China results in the inability of basic-level hospitals to diagnose pterygium patients. To solve this problem, an intelligent-assisted lightweight pterygium diagnosis model based on anterior segment images is proposed in this study. Methods. Pterygium is a common and frequently occurring disease in ophthalmology, and fibrous tissue hyperplasia is both a diagnostic biomarker and a surgical biomarker. The model diagnosed pterygium based on biomarkers of pterygium. First, a total of 436 anterior segment images were collected; then, two intelligent-assisted lightweight pterygium diagnosis models (MobileNet 1 and MobileNet 2) based on raw data and augmented data were trained via transfer learning. The results of the lightweight models were compared with the clinical results. The classic models (AlexNet, VGG16 and ResNet18) were also used for training and testing, and their results were compared with the lightweight models. A total of 188 anterior segment images were used for testing. Sensitivity, specificity, F1-score, accuracy, kappa, area under the concentration-time curve (AUC), 95% CI, size, and parameters are the evaluation indicators in this study. Results. There are 188 anterior segment images that were used for testing the five intelligent-assisted pterygium diagnosis models. The overall evaluation index for the MobileNet2 model was the best. The sensitivity, specificity, F1-score, and AUC of the MobileNet2 model for the normal anterior segment image diagnosis were 96.72%, 98.43%, 96.72%, and 0976, respectively; for the pterygium observation period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 83.7%, 90.48%, 82.54%, and 0.872, respectively; for the surgery period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 84.62%, 93.50%, 85.94%, and 0.891, respectively. The kappa value of the MobileNet2 model was 77.64%, the accuracy was 85.11%, the model size was 13.5 M, and the parameter size was 4.2 M. Conclusion. This study used deep learning methods to propose a three-category intelligent lightweight-assisted pterygium diagnosis model. The developed model can be used to screen patients for pterygium problems initially, provide reasonable suggestions, and provide timely referrals. It can help primary doctors improve pterygium diagnoses, confer social benefits, and lay the foundation for future models to be embedded in mobile devices.

scholarly journals The effect of conjunctivochalasis detected by anterior segment optical coherence tomography on tear function in an elderly population

2020 ◽  
Vol 12 ◽  
pp. 251584142093087
Author(s):  
Dilay Ozek ◽  
Emine Esra Karaca ◽  
Ozlem Evren Kemer
Keyword(s):  
Ocular Surface ◽  
Elderly Population ◽  
Anterior Segment ◽  
Optic Coherence Tomography ◽  
Index Test ◽  
Test Results ◽  
Surface Staining ◽  
Tear Meniscus ◽  
Group 2 ◽  
Group 1

Introduction: The aim of this study was to evaluate conjunctivochalasis (CCH) and its relationship with tear meniscus and tear function in an elderly population. Materials and methods: This prospective, observational study included 144 eyes of 144 patients aged >65 years who were referred to our clinic for various reasons. The patients were separated into group 1 including 64 eyes of 64 patients with CCH and group 2 including 80 eyes of 80 patients without CCH. All patients in both groups underwent a full ophthalmological examination, and the presence of CCH, fluorescein tear break-up time (FTBUT) test, Schirmer test, ocular surface staining (Oxford grading score) and OSDI (Ocular Surface Disease Index) test results were recorded. Measurements of the conjunctivochalasis area (CCHA), tear meniscus height (TMH) and tear meniscus area (TMA) were taken using anterior segment optic coherence tomography (AS-OCT). Results: Group 1 comprised 34 females and 30 males with a mean age of 71.15 ± 12.34 years. Group 2 comprised 43 females and 37 males with a mean age of 68.16 ± 6.05 years ( p = 0.122). The CCH rate was 44.4% in all of the examined patients. The OSDI score and the ocular surface staining test were significantly higher ( p < 0.05), and the FTBUT, TMH and TMA were significantly lower ( p < 0.05) in group 1 than in group 2. The Schirmer I test results were not significantly different between the two groups. Conclusion: The prevalence of CCH is quite high in elderly individuals and may disrupt tear function in these patients.

scholarly journals Assessment of Anterior Segment Changes in Pseudophakic Eyes, Using Ultrasonic Biomicroscopic Imaging, after Pars Plana Vitrectomy with Silicone Oil or Gas Tamponade

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Erkan Ünsal ◽  
Kadir Eltutar ◽  
Belma Karini ◽  
Osman Kızılay
Keyword(s):  
Intraocular Pressure ◽  
Pars Plana Vitrectomy ◽  
Silicone Oil ◽  
Morphological Changes ◽  
Anterior Segment ◽  
Anterior Chamber Depth ◽  
Ciliary Process ◽  
Pars Plana ◽  
Group 2 ◽  
Group 1

Objective. To evaluate the morphological changes of the anterior segment using ultrasonic biomicroscopy (UBM) imaging in pseudophakic patients who underwent pars plana vitrectomy (PPV) with silicone oil or gas (C3F8) internal tamponade agent injection.Method. This prospective study included pseudophakic patients with planned PPV, divided into two groups according to internal tamponade agent: those in which silicone oil was used (n=27, Group 1) and those in which gas (C3F8) was used (n=24, Group 2). UBM measurements were performed in the supine position before and one week after surgery.Results. In patients of Group 1, postoperative trabecular meshwork-ciliary process distance (T-CPD) and iris-ciliary process distance (I-CPD), according to preoperative values, were found to be statistically significantly reduced, and postoperative mean value of scleral thickness (ST) and intraocular pressure (IOP), according to preoperative value, was found to be statistically significantly increased. In patients of Group 2, postoperative mean values of anterior chamber depth (ACD), ciliary body thickness (CBT), T-CPD, I-CPD, and IOP, according to preoperative values, were found to be statistically significantly reduced. Preoperatively, in Group 2 patients, according to Group 1 patients, TIA and IOP were found to be statistically significantly increased. Preoperative and postoperative IOP between the measured parameters with UBM showed no statistically significant correlation.Conclusions. Gases cause more morphological changes in the anterior segment structures. It is thought that complications such as increased intraocular pressure can be seen more frequently for this reason.

scholarly journals Autopsy Patients With Obesity or Metabolic Syndrome as Basic Cause of Death: Are There Pathological Differences Between These Groups?

2018 ◽  
Vol 11 ◽  
pp. 117955571879157
Author(s):  
Lucas Braz Gonçalves ◽  
Helio Amante Miot ◽  
Maria Aparecida Custódio Domingues ◽  
Cristiano Claudino Oliveira
Keyword(s):  
Metabolic Syndrome ◽  
Cause Of Death ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Clinical Aspects ◽  
Macroscopic Findings ◽  
Group 2 ◽  
Definition Of ◽  
Death Group ◽  
Group 1

Background: The objectives of this study were the evaluation of pathological characteristics of patients with obesity or metabolic syndrome (MS) as basic cause of death, associating the autopsy findings with some clinical aspects and the abdominal adipose panicle thickness. Methods: A total of 88 autopsy cases were studied, divided into 2 groups based on the main cause of death: group 1 (n = 15) obesity and group 2 (n = 73) MS. Clinical summaries of autopsy requests, macroscopic findings, and histologic sections were reviewed. Results: The definition of obesity as the basic cause of death is associated with larger thickness of the abdominal adipose panicle, being 8.5 cm ( P = .001) the best measurement, according to the receiver operating characteristic curve. Hypertensive cardiopathy ( P = .001), ischemic cardiopathy ( P = .003), coronary ( P = .008)/systemic ( P = .005) atherosclerosis, and arterial ( P = .014)/arteriolar ( P = .027) nephrosclerosis are associated with the diagnosis of MS. Steatohepatitis is associated with the diagnosis of obesity ( P = .030); however, its association with the thickness of the abdominal adipose panicle is not statistically significant ( P = .211). Conclusions: In the context of an obese patient in autopsy, pathologist may use the information about abdominal adipose panicle associated with heart, kidney, and liver findings, even macroscopic ones, to decide the basic cause death between obesity and MS.

Sign in / Sign up

Export Citation Format

Share Document