A Novel Texture Extraction Method for the Sedimentary Structures’ Classification of Petroleum Imaging Logging

Anemia is one of the common adverse reactions after hip fracture surgery. The traditional method to solve anemia is allogeneic transfusion. However, the transfusion may lead to some complications such as septicemia and fever. So far, few studies have reported roles of machine learning in predicting whether blood transfusion is needed or not after hip fracture surgery. Therefore, the purpose of this study is to develop machine learning models to predict the likelihood of postoperative blood transfusion in patients undergoing hip fracture surgery. This study enrolled 1355 patients who underwent hip fracture surgery at the Affiliated Hospital of Qingdao University from January 2016 to December 2021. Among all patients, 210 cases received postoperative blood transfusion. All patients were randomly divided into a training group and a testing group at a ratio of 7:3. In the training group, univariate and multivariate logistic regression analyses were used to determine independent risk factors for the postoperative transfusion. Then, based on these independent risk factors, tenfold cross-validation method was utilized to develop five machine learning models, including logistic, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The receiver operating characteristic (ROC) curve, area under ROC curve (AUC), and Matthews correlation coefficient (MCC) were generated to evaluate the performance of the models. Calibration plot and decision curve analysis (DCA) were used to test the performance, stability, and clinical applicability of the models. The models were validated using the testing group; and the ROC curve, MCC, calibration plot, and DCA curves were also generated to validate the performance, stability, and clinical applicability of the models. To further verify the robustness of the model, we randomly grabbed 70% of the samples in the testing set, performed 1000 iterations, and calculated the AUC and confidence interval of the five models. Finally, we used SHapley Additive exPlanations (SHAP) to explain these models. Multivariate logistic regression analysis showed that there were 8 independent risk factors, including age, blood transfusion history, albumin (ALB), globulin (GLO), total bilirubin (TBIL), indirect bilirubin (IBIL), hemoglobin (HB), and blood loss > 200ml. We finally selected five independent risk factors including HB, GLO, age, IBIL, and blood loss > 200ml. Based on these five independent risk factors, we generated six characteristic variables, namely HB, HB × HB, HB × blood loss, GLO × HB, age, age × IBIL, and established five machine learning models using a tenfold cross-validation method. In the training group, the AUC values of logistic, RF, MLP, SVM, and XGB were 0.9320, 0.8911, 0.9327, 0.9225, and 0.8825, respectively, and the average AUC was 0.9122 ± 0.0212. The MCC values were 0.65, 0.77, 0.65, 0.66, and 0.68, respectively, and the calibration plot and DCA performed well. In the testing group the AUC values of logistic, RF, MLP, SVM, and XGB were 0.8483, 0.7978, 0.8576, 0.8598, and 0.8216, respectively. The average AUC was 0.8370 ± 0.0238, and the MCC values were 0.41, 0.35, 0.40, 0.41, and 0.41, respectively. The calibration plot and DCA in the testing group also showed good performance. The AUC values and confidence intervals of the 1000-iteration model were: logistic (AUC, min confidence interval [CI]-max confidence interval [CI] 0.848, 0.804-0.903), RF (AUC, minCI-maxCI 0.797, 0.734-0.857), MLP (AUC, minCI-maxCI 0.858, 0.812-0.902), SVM (AUC, minCI-maxCI 0.859, 0.819-0.910), and XGB (AUC, minCI-maxCI 0.821, 0.764-0.894). The model performed well. Finally, according to SHAP, among all five models, HB played the most important role in model prediction and interpretation. The five models we developed all performed well in predicting the likelihood of blood transfusion after hip fracture surgery. Therefore, we believed that the prediction model based on machine learning had great application prospects in clinical practice, which could help clinicians better predict the risk of blood transfusion after hip fracture surgery.

MACHINE LEARNING PREDICTION OF IN-HOSPITAL DISEASE PROGRESSION IN COMMUNITY-ACQUIRED PNEUMONIA: DERIVATION AND VALIDATION OF CLINICAL DETERIORATION IN PNEUMONIA (CDIP)

Active surveillance (AS) has excellent short to medium term outcomes in well-selected prostate cancer patients. Traditional biopsy-based selection criteria have been criticized for inaccurate determination of cancer grade and extent. We evaluated the incremental benefit of multiparametric magnetic resonance imaging (mpMRI) in patient selection using various AS criteria. We retrospectively evaluated men who received mpMRI before radical prostatectomy between 2011 and 2014. Patients were classified as suitable for AS using four criteria: (1) Epstein, (2) National Comprehensive Cancer Network (NCCN) low-risk or (3) extended criteria (Gleason ⩽3+4, PSA ⩽15 ng/ml, clinical stage ⩽T2b) using clinical parameters. The incremental value of mpMRI was evaluated against the referent standard of surgical pathology in determining suitability for AS using sensitivity, specificity, likelihood ratios (LRs) and area under receiver operating curves (AUCs). We evaluated 208 men. Only one man fulfilled Epstein criteria (1) at pathology, who was neither identified using clinical criteria nor mpMRI. Using (2), clinical criteria had a sensitivity of 80%, specificity 75%, LR+ 3.3, LR- 0.3, AUC 0.78, while combined clinical-mpMRI criteria achieved a sensitivity of 80%, specificity 99.5% (P<0.01), LR+ 162, LR- 0.2 and AUC 0.90 (P<0.01 compared to clinical). Using (3), clinical criteria had a sensitivity of 74%, specificity 47%, LR+ 1.4, LR- 0.6, AUC 0.60, while combined clinical-mpMRI criteria achieved a sensitivity of 26% (P<0.01), specificity 97% (P<0.01), LR+ 8.3, LR- 0.8 and AUC 0.62 (P=0.85). Addition of mpMRI significantly improved selection of men for AS using NCCN low-risk criteria. For selecting men with limited prognostic grade group 2, mpMRI significantly improved specificity at the expense of sensitivity.

Extracting efiective features to describe texture is always a key problem in writer identiflcation. This paper proposes a novel method for texture feature extraction by integrating Gabor fllter and Gauss Markov Random Field (GMRF). That is to say, the handwriting images are flrstly flltered by a bank of Gabor fllters in which global features such as directional information can be detected; then GMRF models are developed for every flltered image that is ∞exible enough to capture the local spatial structure and the model parameters of all GMRFs are concatenated as texture features for writer identiflcation, and flnally, Support Vector Machine (SVM) is applied to evaluate the performance of calligraphic artist identiflcation. The experimental results show that this method can achieve a classiflcation rate of 98%, which has outperformed the traditional Gabor fllter method.

One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH. We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each. At population-level, SVM outperforms RF algorithm with a receiver operating characteristic-area under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%). Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.

BackgroundThe process for assessing cognitive symptoms in clinic is time consuming, expensive and usually requires multiple clinicians. There is an urgent need for accessible, quick and low‐cost methods to aid identification of people at the early stages of the dementia continuum. Emerging evidence shows that simple hand movement tests could aid risk stratification. We evaluated the classification accuracy of the new online TAS Test alternate tapping test in the ISLAND Cognitive Clinic in Tasmania, Australia, for dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD) from control subjects.Method212 participants (69.0 ± 9.3 years old) were recruited: 130 from the Clinic (50 diagnosed with dementia, 54 with MCI, 26 with SCD) and 82 cognitively healthy controls. All Clinic participants had a gold‐standard interdisciplinary consensus diagnosis after detailed neuropsychological tests and completed a 60‐second alternate key tapping test on the TAS Test website during their standard clinic visit. Healthy controls completed the Addenbrookes Cognitive Examination with a score of 95 or higher and TAS Test in clinic. Frequency, variability, key press duration and accuracy scores were calculated. Regression models comprising all motor features, adjusted for age and sex, were compared to their respective null models, comprising age and sex, with area under ROC curves (AUC).ResultCompared to the baseline model, tapping motor data improved classification of people with dementia (AUC = 0.89, 95% CI = [0.84,0.95], p=0.025), MCI (AUC = 0.8, 95% CI = [0.73,0.87], p=0.002) and SCD (AUC = 0.75, 95% CI = [0.63,0.86], p = 0.004) from cognitively healthy controls. The motor features also helped distinguish dementia (AUC = 0.9, 95% CI = [0.84,0.97], p = 0.024) and MCI (AUC = 0.79, 95% CI = [0.67,0.91], p = 0.022) from SCD, but not dementia from MCI (p = 0.221).ConclusionA 60‐second simple keyboard tapping test helped distinguish people with SCD, MCI, or dementia from cognitively healthy controls and holds potential as a new low‐cost and clinically‐accessible motor biomarker.

BackgroundThe Inflammatory Bowel Disease Symptom Inventory (IBDSI) is a validated patient self-reported measure used to assess IBD disease activity.PurposeWe aimed to assess the prevalence of symptoms, and examine which symptoms are most associated with disease activity as measured by a symptom index and objective measure of inflammation.MethodThe Manitoba Living with IBD Study is a prospective study of 156 participants with confirmed IBD who completed bi-weekly Inflammatory Bowel Disease Symptom Inventory (IBDSI) surveys. Relative risks (RR) (with 95% confidence interval (CI)), positive and negative predictive values (PPV, NPV), and area under receiver operator curve (AUC) were reported for each symptom to predict active disease defined as: (1) active IBDSI, (2) self-reported flare, and (3) elevated fecal calprotectin (FCAL) (>250µg/g). Analyses were undertaken following stratification based on sex, and disease type (Crohn’s disease (CD) and ulcerative colitis (UC)).Result(s)In total, 69.2% of participants were female; 64.7% had CD. Fatigue was the most prevalent symptom in both inactive and active disease, across all 3 disease measures (IBDSI: 24.5% and 75.1%, self-reported flare: 42.2% and 72.2%, FCAL: 46.0% and 60.6%). Absence of fatigue had a high NPV for active IBDSI and self-reporting a flare in both CD and UC. Urgency had a consistently strong NPV, RR and AUC across all three disease measures in both IBD subtypes and sexes. The number of loose/liquid bowel movements predicted elevated FCAL in UC (RR in men = 3.5, 95% CI 1.2-9.9, RR in women = 1.8, 95% CI 1.2-2.7, AUC in men = 0.73, 95% CI 0.59-0.84, AUC in women = 0.65, 95% CI 0.56-0.72), as did blood in stool in females with UC (RR = 1.8, 95% CI 1.2-2.7). In men with CD, excessive bowel gas (RR = 2.0, 95% CI 1.2-3.4) and urgency (RR = 3.9, 95% CI 1.6-9.3) best predicted an elevated FCAL. No symptom was strongly predictive of elevated FCAL in females with CD.Conclusion(s)Fatigue was the most prevalent symptom irrespective of disease activity. Urgency stood out as being consistently associated with disease activity, irrespective of the disease measure. Individual symptoms have different impacts on subjective (IBDSI) and objective (FCAL) measures in IBD.Please acknowledge all funding agencies by checking the applicable boxes belowNoneDisclosure of InterestNone Declared

In quality improvement in medical care one important aim is to prevent complications after a surgery and, particularly, keep the mortality rate as small as possible. Therefore it is of great importance to identify which factors increase the risk to die in the aftermath of a surgery. Based on data of 1,163 patients who underwent an isolated coronary bypass surgery in 2007 or 2008 at the Clinic of Cardiovascular Surgery in Dusseldorf, Germany, we select predictors that affect the in-hospital-mortality. A forward search using the wrapper approach in conjunction with simple linear and also more complex classification methods such as gradient boosting and support vector machines is performed. Since the classification problem is highly imbalanced with certainly unequal but unknown misclassification costs the area under ROC curve (AUC) is used as performance criterion for hyperparameter tuning as well as for variable selection. In order to get stable results and to obtain estimates of the AUC the variable selection is repeated 25 times on different subsamples of the data set. It turns out that simple linear classification methods (linear discriminant analysis and logistic regression) are suitable for this problem since the AUC cannot be considerably increased by more complex methods. We identify the three most important predictors as the severity of cardiac insufficiency, the patient’s age as well as pulmonary hypertension. A comparison with full models trained on the same 25 subsamples shows that the classification performance in terms of AUC is not affected or only slightly decreased by variable selection.

Different medical data mining approaches based prediction of ischemic stroke

We assessed the role of urine lipoarabinomannan (LAM) grade and a second LAM test for HIV-associated pulmonary tuberculosis (TB) screening in outpatient clinics in South Africa. We enrolled newly diagnosed HIV-infected adults (≥18 years) at 4 clinics, excluding those on TB therapy. Participants provided sputum for acid-fast bacilli (AFB) microscopy and culture. Nurses conducted 2 rapid urine LAM tests at the point-of-care and graded positive results from low (faint) to high (5+). Culture-confirmed pulmonary TB was the gold standard. We used area under receiver operating curves (AUROC) to compare screening strategies. Among 320 HIV-infected adults, median CD4 was 248 cells per cubic millimeter (interquartile range, 107-379/mm); 54 (17%) were TB culture positive. Fifty-two (16%) of all participants were LAM positive by either test; correlation between LAM tests was high. Among 10 "faint" positive results, 2 (20%) had culture-positive TB. Using ≥1+ LAM grade as positive, 1 LAM test had sensitivity of 41% [95% confidence interval (CI): 28% to 55%] and specificity of 92% (95% CI: 88% to 95%). A 2 LAM test strategy had a sensitivity of 43% (95% CI: 29% to 57%). One LAM test ≥1+ grade (AUROC = 0.66; 95% CI: 0.60 to 0.73) was significantly better than sputum AFB alone. The optimal strategy was sequentially performing 1 LAM test followed by sputum AFB if LAM grade <1+ (AUROC = 0.70; 95% CI: 0.63 to 0.77), which had sensitivity of 48% (95% CI: 34% to 62%) and specificity of 91% (95% CI: 87% to 94%). In this clinic-based study, "faint" line was a false-positive second urine LAM test added no value, and an optimal screening strategy was 1 LAM test followed by sputum AFB microscopy for urine LAM-negative people.

Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced significantly if the precise early prediction is possible. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labeled data and also the presence of outliers (or missing values) in the diabetes datasets. In this literature, we are proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection, K-fold cross-validation, and different Machine Learning (ML) classifiers (k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, and XGBoost) and Multilayer Perceptron (MLP) were employed. The weighted ensembling of different ML models is also proposed, in this literature, to improve the prediction of diabetes where the weights are estimated from the corresponding Area Under ROC Curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyperparameter tuning using the grid search technique. All the experiments, in this literature, were conducted under the same experimental conditions using the Pima Indian Diabetes Dataset. From all the extensive experiments, our proposed ensembling classifier is the best performing classifier with the sensitivity, specificity, false omission rate, diagnostic odds ratio, and AUC as 0.789, 0.934, 0.092, 66.234, and 0.950 respectively which outperforms the state-of-the-art results by 2.00 % in AUC. Our proposed framework for the diabetes prediction outperforms the other methods discussed in the article. It can also provide better results on the same dataset which can lead to better performance in diabetes prediction. Our source code for diabetes prediction is made publicly available.

Backgrounds: Sentinel lymph node (SLN) biopsy is a highly accurate predictor of axillary status and has become the surgical axillary standard in breast cancer patients. About 50–70 % of patients with involved SLN have no additional non sentinel node (NSN) involved, suggesting that it be possible to avoid ALND in selected patients. Many tools have been developed to help surgeons in NSLN evaluation but they all need pathological data from tumor and SLN and can't be used during surgery. Developed for intraoperative detection of SLN macro or micrometastasis involvement, the semi-automated molecular one step nucleic acid amplification (OSNA), as accurate as pathology, is available. Two simple nomograms have been developed to predict NSN involvement based on the number of CK19 mRNA copy determined by OSNA: · Nomogram developed by Peg V (Eur J Surg Oncol 2013): based on total tumoral load (TTL). TTL is defined as the addition of CK19 mRNA copies of each positive SLN (copies/μL). A TTL≥1.2 × 10(5) copies/ml (specificity=85.3%, negative predictive value (NPV) = 80%) can predict NSN involvement. · Nomogram developed by Di Filippo F (Journal of Experimental &amp; Clinical Cancer Research 2015): based on the number of CK19 mRNA copies and ultrasound tumor size. These two variables are categorized using quartiles with a score for each and the addition of both corresponds to a probability of NSN involvement (sensitivity = 98.1%, NPV = 92.5 %). Patients and Methods: this is a retrospective study of 299 patients. Each patient had SLN involvement (macro or micrometastasis) and underwent a complementary ALND. The main objective was to evaluate the performance of each nomogram using a discrimination ability model, assessed by ROC analysis. Predictive accuracy was measured by the area under ROC curves (AUC) reported with its 95 % confidence interval. The second objective was to compare the two nomograms using Hanley &amp; McNeil method, to test the statistical significance of the difference between the AUC. Analysis was performed using stata 13.1 SE. Results: The mean age was 59, 1 year. Most patients were treated for an infiltrating ductal carcinoma (80.3%, 240/299). The mean ultrasound tumor size was 13 mm and the mean pathological tumor size was 15 mm. The median number of examined SLN was 2 with a macro-metastasis in 67, 6%, 202/299). 70 patients had involved nodes in ALND (23%). The discrimination of N Peg, quantified with AUC was 0.685 (p&amp;lt;0, 00001). The discrimination of N Di Filippo, quantified with AUC was 0.72 (p&amp;lt;0, 00001). Hanley &amp; McNeil method shows that Di Filippo nomogram is significantly superior to Peg nomogram (p=0,048). Conclusion: The current study shows that these two nomograms are reliable and can be used to predict NSLN involvement. The combination of molecular data and ultrasound tumor size seems to be more efficient than molecular data alone. These results are similar to results of nomogram studies based on pathological analysis but only these nomograms integrating molecular data can be used during the surgery. Citation Format: Bordes V, Campion L, Jezequel P, Lefrancois A, Boiffard F, Brillaud-Meflah V, Dravet F, Jaffre I, Classe J-M. Non-sentinel lymph nodes involvement in early breast cancer patients: Performance of two predictive nomograms integrating the analysis of sentinel nodes by one step nucleic acid amplification in a cohort of 299 patients [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P2-01-33.

To evaluate the diagnostic value of circulating microRNA-1 (miR-1) in early coronary artery plaque rupture in patients with stable coronary artery disease (SCAD). A prospective cohort study was conducted. Sixty-seven patients with SCAD admitted to the department of cardiology of the Third Affiliated Hospital of Soochow University from January to June in 2019 were enrolled. All patients had completed coronary angiography (CAG), percutaneous coronary intervention (PCI) single stent implantation or only CAG was performed according to the CAG results. Blood samples were collected before (0 hour) and 3 hours after the procedure. The expression of plasma miR-1 was detected by real-time quantitative reverse transcription-polymerase chain reaction (RT-PCR), and electrocardiogram was used to detect cardiac troponin I (cTnI) levels. The difference of miR-1 and cTnI levels in PCI or CAG patients before and after procedure were compared, and the value for early diagnosis of coronary artery plaque rupture in SCAD patients was evaluated. The diagnostic efficacy was evaluated by the receiver operating characteristic curve (ROC curve). There were 38 CAG patients and 29 PCI patients. There were no significant differences in gender, age, previous history (without hypertension history) and baseline data of cardiac function between the two groups. The expression of miR-1 after PCI was significantly higher than that before PCI [2-ΔΔCt: 2.11 (1.56, 2.73) vs. 1.26 (1.07, 1.92), P < 0.01], and there was no significant difference in cTnI level before and after PCI [μg/L: 0.00 (0.00, 0.02) vs. 0.00 (0.00, 0.02), P > 0.05]. There were no significant differences in miR-1 and cTnI levels before and after procedure in the CAG group [miR-1 (2-ΔΔCt): 1.09 (1.00, 1.40) vs. 1.21 (1.00, 1.71), cTnI (μg/L): 0.00 (0.00, 0.02) vs. 0.00 (0.00, 0.02), both P > 0.05]. ROC curve analysis showed that the area under ROC curve (AUC) and 95% confidence interval (95%CI) of miR-1 in the diagnosis of coronary plaque rupture were 0.794 (0.687-0.900), P < 0.01, the sensitivity was 82.8%, the specificity was 68.4%, and the optimal cut-off value was 1.51. The AUC and 95%CI of the difference of miR-1 before and after operation (ΔmiR-1) were 0.704 (0.567-0.842), P = 0.004, the sensitivity was 62.1%, the specificity was 84.2%, and the optimal cut-off value was 0.39. The efficancy of miR-1 and ΔmiR-1 after procedure to diagnose coronary plaque rupture in patients with SCAD was similar (Z = 1.287, P = 0.198). However, baseline miR-1 might not predict whether patients with SCAD need PCI or not (AUC = 0.630, P > 0.05). Multivariate binary Logistic regression analysis showed that increased postoperative miR-1 expression was an independent risk factor for coronary plaque rupture in SCAD patients [odds ratios (OR) = 2.887, 95%CI was 1.044-7.978, P = 0.041]. Circulating miR-1 might have the value for early diagnosis of coronary artery plaque rupture in SCAD patients.

BackgroundCoronary computed tomography angiography stenosis score (CCTA-SS) is a proposed diagnosis score that considers the plaque characteristics, myocardial function, and the diameter reduction rate of the lesions. This study aimed to evaluate the diagnostic performance of the CCTA-SS in seeking coronary artery disease (CAD).MethodsThe 228 patients with suspected CAD who underwent CCTA and invasive coronary angiography (ICA) procedures were under examination. The diagnostic performance was evaluated with the receiver operating curve (ROC) for CCTA-SS in detecting CAD (defined as a diameter reduction of ≥ 50%) and severe CAD (defined as a diameter reduction of ≥ 70%).ResultsThe area under ROC (AUC) of CCTA-SS was 0.909 (95% CI: 0.864–0.943), which was significantly higher than that of CCTA (AUC: 0.826; 95% CI: 0.771–0.873; P = 0.0352) in diagnosing of CAD with a threshold of 50%. The optimal cutoff point of CCTA-SS was 51% with a sensitivity of 90.66%, specificity of 95.65%, positive predictive value of 98.80%, negative predictive value of 72.13%, and accuracy of 91.67%, whereas the optimal cutoff point of CCTA was 55%, and the corresponding values were 87.36%, 93.48%, 98.15%, 65.15%, and 88.60%, respectively. With a threshold of 70%, the performance of CCTA-SS with an AUC of 0.927 (95% CI: 0.885–0.957) was significantly higher than that of CCTA with an AUC of 0.521 (95% CI: 0.454–0.587) (P < 0.0001).ConclusionsCCTA-SS significantly improved the diagnostic accuracy of coronary stenosis, including CAD and severe CAD, compared with CCTA.

Objective: To investigate the value of magnetic resonance imaging (MRI) in predicting the type and severity of placenta accreta (PA). Methods: The clinical data of 67 PA cases in Peking University Third Hospital from January, 2015 to November, 2016 were analyzed retrospectively.Cases were classified according to the direct and indirect MRI signs.Chi-square test was applied to disclose the difference between different PA types and MRI signs.MRI signs with statistical significance were further calculated by the area under ROC curve (AUC) value analysis to reveal the correlation between specific signs and the type/severity of PA. Results: Among the 67 cases, 24 cases were of mild PA and 43 cases were of severe PA.Statistical difference was found between the two PA groups in some signs such as placenta tissues invading the uterus myometrium, placenta tissues extending beyond the uterus outline, the uniformity of placental signal, abnormal vessel imaging in the placenta basilar part, Bladder Tent Sign and the discontinuity of low signal zone in the gap between bladder and placenta.AUC analysis showed that the above MRI signs had the stronger correlations between the type/severity of PA, compared with other signs. Conclusion: Not only can MRI be used to predict the type of PA, but also it can be used to predict the location and severity of PA, which has an important reference value for clinical works.