Automated detection of steno-occlusive lesion on time-of-flight magnetic resonance angiography: an observer performance study.

BACKGROUND AND PURPOSE: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an artificial intelligence model for detecting steno-occlusive lesions in the intracranial arteries. MATERIALS AND METHODS: Overall, 138 TOF-MRA images were collected from two institutions, which served as internal (n = 62) and external (n = 76) test sets, respectively. Each study was reviewed by five radiologists (two neuroradiologists and three radiology residents) to compare the usage and non-usage of our proposed artificial intelligence model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed using the area under the Jackknife free-response receiver operating characteristic curve and reading time for comparison. RESULTS: The average area under the Jackknife free-response receiver operating characteristic curve for the five radiologists demonstrated an improvement from 0.70 without artificial intelligence to 0.76 with artificial intelligence (P = .027). Notably, this improvement was most pronounced among the three radiology residents, whose performance metrics increased from 0.68 to 0.76 (P = .002). Despite an increased reading time upon using artificial intelligence, there was no significant change among the readings by radiology residents. Moreover, the use of artificial intelligence resulted in improved inter-observer agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752). CONCLUSIONS: Our proposed artificial intelligence model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less-experienced readers may benefit the most from this model.ABBREVIATIONS: AI = Artificial intelligence; AUC = Area under the receiver operating characteristic curve; AUFROC = Area under the Jackknife free-response receiver operating characteristic curve; DL = Deep learning; ICC = Intraclass correlation coefficient; IRB = Institutional Review Boards; JAFROC = Jackknife free-response receiver operating characteristic.

Bojungikgi-tang for Chemotherapy-induced Leukopenia: A Systematic Review and Meta-Analysis.

Chemotherapy-induced leukopenia is a common side effect of cytotoxic anticancer drugs. It can deprive patients of treatment opportunities, resulting in the delay, reduction, or discontinuation of chemotherapy or other anticancer drug administration. Two researchers searched English, Chinese, Japanese, and Korean electronic databases, without limiting the time period and language, using search terms such as "Bojungikgi," "WBC," "leuko," and "neutrop." Among the human randomized controlled studies in which Bojungikgi-tang was administered to patients who underwent chemotherapy, studies reporting leukopenia-related outcomes were selected, and data extraction, bias risk assessment, and meta-analysis were performed on the selected papers. Ten studies were selected, and a systematic review with meta-analysis was conducted. Nine papers were published in China and the total number of participants was 715. As a result of administering Bojungikgi-tang to these patients, the number of patients with chemotherapy-induced leukopenia significantly decreased (OR: 0.41, 95% CI: 0.27-0.61, P = .0001, I2 = 35%). Further, white blood cell counts were compared with that of the control group, and it showed an effect on prevention (MD: 0.64, 95% CI: 0.46-0.83, P < .00001, I2 = 90%). A pronounced effect was observed, especially when administered after a diagnosis based on the pattern identification, such as Qi deficiency. (OR: 0.32, 95% CI: 0.18-0.58, P = .0002, I2 = 0%). However, all studies had a high risk of bias due to non-blinding, and most studies had a high or uncertain risk of bias in creating random assignment orders and concealing them. Bojungikgi-tang has an effect on the prevention and treatment of chemotherapy-induced leukopenia. The effect rate can be increased when administered after proper diagnosis, and the possibility of adverse reactions and side effects is lower than that of Granulocyte-Colony Stimulating Factor (G-CSF) injection. Bojungikgi-tang appears to be useful in the treatment and prevention of leukopenia caused by cytotoxic anticancer drugs. However, it is necessary to conduct high-quality clinical studies in the future, considering the possibility of local and language bias, heterogeneity of carcinoma and intervention, and the risk of bias.Registration: PROSPERO CRD4202341054.

Ultrastrong and ductile steel welds achieved by fine interlocking microstructures with film-like retained austenite.

The degradation of mechanical properties caused by grain coarsening or the formation of brittle phases during welding reduces the longevity of products. Here, we report advances in the weld quality of ultra-high strength steels by utilizing Nb and Cr instead of Ni. Sole addition of Cr, as an alternative to Ni, has limitations in developing fine weld microstructure, while it is revealed that the coupling effects of Nb and Cr additions make a finer interlocking weld microstructures with a higher fraction of retained austenite due to the decrease in austenite to acicular ferrite and bainite transformation temperature and carbon activity. As a result, an alloying design with Nb and Cr creates ultrastrong and ductile steel welds with enhanced tensile properties, impact toughness, and fatigue strength, at 45% lower material costs and lower environmental impact by removing Ni.

Application of symmetry evaluation to deep learning algorithm in detection of mastoiditis on mastoid radiographs.

As many human organs exist in pairs or have symmetric appearance and loss of symmetry may indicate pathology, symmetry evaluation on medical images is very important and has been routinely performed in diagnosis of diseases and pretreatment evaluation. Therefore, applying symmetry evaluation function to deep learning algorithms in interpreting medical images is essential, especially for the organs that have significant inter-individual variation but bilateral symmetry in a person, such as mastoid air cells. In this study, we developed a deep learning algorithm to detect bilateral mastoid abnormalities simultaneously on mastoid anterior-posterior (AP) views with symmetry evaluation. The developed algorithm showed better diagnostic performance in diagnosing mastoiditis on mastoid AP views than the algorithm trained by single-side mastoid radiographs without symmetry evaluation and similar to superior diagnostic performance to head and neck radiologists. The results of this study show the possibility of evaluating symmetry in medical images with deep learning algorithms.

Intracranial steno-occlusive lesion detection on time-of-flight MR angiography using multi-task learning.

Steno-occlusive lesions in intracranial arteries refer to segments of narrowed or occluded blood vessels that increase the risk of ischemic strokes. Steno-occlusive lesion detection is crucial in clinical settings; however, automatic detection methods have hardly been studied. Therefore, we propose a novel automatic method to detect steno-occlusive lesions in sequential transverse slices on time-of-flight magnetic resonance angiography. Our method simultaneously detects lesions while segmenting blood vessels based on end-to-end multi-task learning, reflecting that the lesions are closely related to the connectivity of blood vessels. We design classification and localization modules that can be attached to arbitrary segmentation network. As blood vessels are segmented, both modules simultaneously predict the presence and location of lesions for each transverse slice. By combining outputs from the two modules, we devise a simple operation that boosts the performance of lesion localization. Experimental results show that lesion prediction and localization performance is improved by incorporating blood vessel extraction. Our ablation study demonstrates that the proposed operation enhances lesion localization accuracy. We also verify the effectiveness of multi-task learning by comparing our approach with those that individually detect lesions with extracted blood vessels.

Differential diagnosis of common etiologies of left ventricular hypertrophy using a hybrid CNN-LSTM model.

Differential diagnosis of left ventricular hypertrophy (LVH) is often obscure on echocardiography and requires numerous additional tests. We aimed to develop a deep learning algorithm to aid in the differentiation of common etiologies of LVH (i.e. hypertensive heart disease [HHD], hypertrophic cardiomyopathy [HCM], and light-chain cardiac amyloidosis [ALCA]) on echocardiographic images. Echocardiograms in 5 standard views (parasternal long-axis, parasternal short-axis, apical 4-chamber, apical 2-chamber, and apical 3-chamber) were obtained from 930 subjects: 112 with HHD, 191 with HCM, 81 with ALCA and 546 normal subjects. The study population was divided into training (n = 620), validation (n = 155), and test sets (n = 155). A convolutional neural network-long short-term memory (CNN-LSTM) algorithm was constructed to independently classify the 3 diagnoses on each view, and the final diagnosis was made by an aggregate network based on the simultaneously predicted probabilities of HCM, HCM, and ALCA. Diagnostic performance of the algorithm was evaluated by the area under the receiver operating characteristic curve (AUC), and accuracy was evaluated by the confusion matrix. The deep learning algorithm was trained and verified using the training and validation sets, respectively. In the test set, the average AUC across the five standard views was 0.962, 0.982 and 0.996 for HHD, HCM and CA, respectively. The overall diagnostic accuracy was significantly higher for the deep learning algorithm (92.3%) than for echocardiography specialists (80.0% and 80.6%). In the present study, we developed a deep learning algorithm for the differential diagnosis of 3 common LVH etiologies (HHD, HCM and ALCA) by applying a hybrid CNN-LSTM model and aggregate network to standard echocardiographic images. The high diagnostic performance of our deep learning algorithm suggests that the use of deep learning can improve the diagnostic process in patients with LVH.

Mesenchymal Stem Cell-Derived Exosomes and Intervertebral Disc Regeneration: Review.

Intervertebral disc degeneration (IVDD) is a common cause of lower back pain (LBP), which burdens individuals and society as a whole. IVDD occurs as a result of aging, mechanical trauma, lifestyle factors, and certain genetic abnormalities, leads to loss of nucleus pulposus, alteration in the composition of the extracellular matrix, excessive oxidative stress, and inflammation in the intervertebral disc. Pharmacological and surgical interventions are considered a boon for the treatment of IVDD, but the effectiveness of those strategies is limited. Mesenchymal stem cells (MSCs) have recently emerged as a possible promising regenerative therapy for IVDD due to their paracrine effect, restoration of the degenerated cells, and capacity for differentiation into disc cells. Recent investigations have shown that the pleiotropic effect of MSCs is not related to differentiation capacity but is mediated by the secretion of soluble paracrine factors. Early studies have demonstrated that MSC-derived exosomes have therapeutic potential for treating IVDD by promoting cell proliferation, tissue regeneration, modulation of the inflammatory response, and reduced apoptosis. This paper highlights the current state of MSC-derived exosomes in the field of treatment of IVDD with further possible future developments, applications, and challenges.

Tumor grading of soft tissue sarcomas: Assessment with whole-tumor histogram analysis of apparent diffusion coefficient.

PURPOSE: To evaluate the usefulness of whole-tumor ADC histogram analysis based on entire tumor volume in determining the histologic grade of STS (soft tissue sarcoma)s. METHODS: From January 2015 to December 2020, 53 patients with STS who underwent preoperative magnetic resonance imaging, including diffusion weighted imaging and ADC maps (b = 0 and 1400 s/mm2), within 1 month before surgical resection were included in the study. Regions of interest were drawn on every section of the ADC map containing tumor and were summated to derive volume-based histogram data of the entire tumor. Histogram parameters were correlated with histologic tumor grade using Kruskal-Wallis test and compared between high-(grade II and III) and low-grade STSs (grade I) using Mann-Whitney U test. Multivariable logistic regression analysis was applied to identify significant histogram parameters for high-grade STS prediction, and receiver operating characteristic curves (AUC) were constructed to determine optimum threshold. RESULTS: Eight patients with low-grade STS (15.1%) and 45 with high-grade STS (26.4% [14/53] for grade II; 58.5% [31/53] for grade III) were included. High-grade STS showed positive skewness and low-grade STS showed negative skewness (0.503 vs -0.726, p=.001). High-grade STS showed lower mean ADC (p =.03) and 5th to 50th percentile values (p ≤. 03) than those of low-grade STS. Positive skewness was an independent predictor of high-grade STS (odds ratio: 6.704, p=.002) with 84.4% sensitivity and 87.5% specificity (cut-off values > -0.1757, AUC = 0.842). CONCLUSION: Skewness is the most promising histogram parameter for discriminating high-grade from low-grade STS. The mean ADC values and lower half of percentile values are helpful for differentiating high from low-grade STSs.

Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning.

OBJECTIVE: To develop a deep learning algorithm capable of evaluating subscapularis tendon (SSC) tears based on axillary lateral shoulder radiography. METHODS: A total of 2,779 axillary lateral shoulder radiographs (performed between February 2010 and December 2018) and the patients' corresponding clinical information (age, sex, dominant side, history of trauma, and degree of pain) were used to develop the deep learning algorithm. The radiographs were labeled based on arthroscopic findings, with the output being the probability of an SSC tear exceeding 50% of the tendon's thickness. The algorithm's performance was evaluated by determining the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, negative predictive value (NPV), and negative likelihood ratio (LR-) at a predefined high-sensitivity cutoff point. Two different test sets were used, with radiographs obtained between January and December 2019; Test Set 1 used arthroscopic findings as the reference standard (n = 340), whereas Test Set 2 used MRI findings as the reference standard (n = 627). RESULTS: The AUCs were 0.83 (95% confidence interval, 0.79-0.88) and 0.82 (95% confidence interval, 0.79-0.86) for Test Sets 1 and 2, respectively. At the high-sensitivity cutoff point, the sensitivity, NPV, and LR- were 91.4%, 90.4%, and 0.21 in Test Set 1, and 90.2%, 89.5%, and 0.21 in Test Set 2, respectively. Gradient-weighted Class Activation Mapping identified the subscapularis insertion site at the lesser tuberosity as the most sensitive region. CONCLUSION: Our deep learning algorithm is capable of assessing SSC tears based on changes at the lesser tuberosity on axillary lateral radiographs with moderate accuracy. KEY POINTS: • We have developed a deep learning algorithm capable of assessing SSC tears based on changes at the lesser tuberosity on axillary lateral radiographs and previous clinical data with moderate accuracy. • Our deep learning algorithm could be used as an objective method to initially assess SSC integrity and to identify those who would and would not benefit from further investigation or treatment.

Can Deep Learning Using Weight Bearing Knee Anterio-Posterior Radiograph Alone Replace a Whole-Leg Radiograph in the Interpretation of Weight Bearing Line Ratio?

Weight bearing whole-leg radiograph (WLR) is essential to assess lower limb alignment such as weight bearing line (WBL) ratio. The purpose of this study was to develop a deep learning (DL) model that predicts the WBL ratio using knee standing AP alone. Total of 3997 knee AP & WLRs were used. WBL ratio was used for labeling and analysis of prediction accuracy. The WBL ratio was divided into seven categories (0, 0.1, 0.2, 0.3, 0.4, 0.5, and 0.6). After training, performance of the DL model was evaluated. Final performance was evaluated using 386 subjects as a test set. Cumulative score (CS) within error range 0.1 was set with showing maximum CS in the validation set (95% CI, 0.924-0.970). In the test set, mean absolute error was 0.054 (95% CI, 0.048-0.061) and CS was 0.951 (95% CI, 0.924-0.970). Developed DL algorithm could predict the WBL ratio on knee standing AP alone with comparable accuracy as the degree primary physician can assess the alignment. It can be the basis for developing an automated lower limb alignment assessment tool that can be used easily and cost-effectively in primary clinics.

Pre-existing and machine learning-based models for cardiovascular risk prediction.

Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40-79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70-0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer-Lemeshow χ2 = 86.1, P < 0.001) than PCE for whites did (Hosmer-Lemeshow χ2 = 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.

Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs.

Accurate image interpretation of Waters' and Caldwell view radiographs used for sinusitis screening is challenging. Therefore, we developed a deep learning algorithm for diagnosing frontal, ethmoid, and maxillary sinusitis on both Waters' and Caldwell views. The datasets were selected for the training and validation set (n = 1403, sinusitis% = 34.3%) and the test set (n = 132, sinusitis% = 29.5%) by temporal separation. The algorithm can simultaneously detect and classify each paranasal sinus using both Waters' and Caldwell views without manual cropping. Single- and multi-view models were compared. Our proposed algorithm satisfactorily diagnosed frontal, ethmoid, and maxillary sinusitis on both Waters' and Caldwell views (area under the curve (AUC), 0.71 (95% confidence interval, 0.62-0.80), 0.78 (0.72-0.85), and 0.88 (0.84-0.92), respectively). The one-sided DeLong's test was used to compare the AUCs, and the Obuchowski-Rockette model was used to pool the AUCs of the radiologists. The algorithm yielded a higher AUC than radiologists for ethmoid and maxillary sinusitis (p = 0.012 and 0.013, respectively). The multi-view model also exhibited a higher AUC than the single Waters' view model for maxillary sinusitis (p = 0.038). Therefore, our algorithm showed diagnostic performances comparable to radiologists and enhanced the value of radiography as a first-line imaging modality in assessing multiple sinusitis.

Performance of deep learning to detect mastoiditis using multiple conventional radiographs of mastoid.

OBJECTIVES: This study aimed to compare the diagnostic performance of deep learning algorithm trained by single view (anterior-posterior (AP) or lateral view) with that trained by multiple views (both views together) in diagnosis of mastoiditis on mastoid series and compare the diagnostic performance between the algorithm and radiologists. METHODS: Total 9,988 mastoid series (AP and lateral views) were classified as normal or abnormal (mastoiditis) based on radiographic findings. Among them 792 image sets with temporal bone CT were classified as the gold standard test set and remaining sets were randomly divided into training (n = 8,276) and validation (n = 920) sets by 9:1 for developing a deep learning algorithm. Temporal (n = 294) and geographic (n = 308) external test sets were also collected. Diagnostic performance of deep learning algorithm trained by single view was compared with that trained by multiple views. Diagnostic performance of the algorithm and two radiologists was assessed. Inter-observer agreement between the algorithm and radiologists and between two radiologists was calculated. RESULTS: Area under the receiver operating characteristic curves of algorithm using multiple views (0.971, 0.978, and 0.965 for gold standard, temporal, and geographic external test sets, respectively) showed higher values than those using single view (0.964/0.953, 0.952/0.961, and 0.961/0.942 for AP view/lateral view of gold standard, temporal external, and geographic external test sets, respectively) in all test sets. The algorithm showed statistically significant higher specificity compared with radiologists (p = 0.018 and 0.012). There was substantial agreement between the algorithm and two radiologists and between two radiologists (κ = 0.79, 0.8, and 0.76). CONCLUSION: The deep learning algorithm trained by multiple views showed better performance than that trained by single view. The diagnostic performance of the algorithm for detecting mastoiditis on mastoid series was similar to or higher than that of radiologists.

Can Additional Patient Information Improve the Diagnostic Performance of Deep Learning for the Interpretation of Knee Osteoarthritis Severity.

The study compares the diagnostic performance of deep learning (DL) with that of the former radiologist reading of the Kellgren-Lawrence (KL) grade and evaluates whether additional patient data can improve the diagnostic performance of DL. From March 2003 to February 2017, 3000 patients with 4366 knee AP radiographs were randomly selected. DL was trained using knee images and clinical information in two stages. In the first stage, DL was trained only with images and then in the second stage, it was trained with image data and clinical information. In the test set of image data, the areas under the receiver operating characteristic curve (AUC)s of the DL algorithm in diagnosing KL 0 to KL 4 were 0.91 (95% confidence interval (CI), 0.88-0.95), 0.80 (95% CI, 0.76-0.84), 0.69 (95% CI, 0.64-0.73), 0.86 (95% CI, 0.83-0.89), and 0.96 (95% CI, 0.94-0.98), respectively. In the test set with image data and additional patient information, the AUCs of the DL algorithm in diagnosing KL 0 to KL 4 were 0.97 (95% confidence interval (CI), 0.71-0.74), 0.85 (95% CI, 0.80-0.86), 0.75 (95% CI, 0.66-0.73), 0.86 (95% CI, 0.79-0.85), and 0.95 (95% CI, 0.91-0.97), respectively. The diagnostic performance of image data with additional patient information showed a statistically significantly higher AUC than image data alone in diagnosing KL 0, 1, and 2 (p-values were 0.008, 0.020, and 0.027, respectively).The diagnostic performance of DL was comparable to that of the former radiologist reading of the knee osteoarthritis KL grade. Additional patient information improved DL diagnosis in interpreting early knee osteoarthritis.

Acupuncture for Regulating Blood Pressure of Stroke Patients: A Systematic Review and Meta-Analysis.

Background: Blood pressure (BP) after a stroke is affected by autonomic regulation, the Cushing reaction, and basal history of hypertensive, arteriosclerotic conditions. To prevent recurrent stroke attack and its complications, BP must be regulated to within the normal range through monitoring, rest, and medication. Previous studies have reported that acupuncture may be effective in lowering BP in patients with hypertension. Objectives: This study was aimed at evaluating the efficacy of acupuncture in regulating BP in stroke patients, including both cerebral infarction and hemorrhage. Methods: A review was conducted of articles published in English, Korean, Chinese, and Japanese across 16 electronic databases (Pubmed, EMBASE, Cochrane Central Resister of Controlled Trials, AMED, CINAHL, CNKI, Wanfang, VIP, CiNii, and seven Korean databases) up to April 2020. Only randomized controlled trials that evaluated the efficacy of acupuncture for stroke patients were included and meta-analyzed, and BP data and risk of bias were extracted by scanning the full texts. Data analysis was performed by using RevMan 5.3. Results: From the 16 electronic databases, 7623 relevant articles were identified, and 847 stroke patients of 10 trials met the inclusion criteria. Two trials reported that BP was lowered more in the group who had received acupuncture treatment than the group who were treated with conventional medication. Two trials reported that BP was lowered after auricular acupuncture treatment more than those observed in the group receiving conventional medical treatment. Six trials reported that BP in cerebral infarction patients was lower than in the control group. None of the trials reported any adverse events. Conclusions: It was concluded that acupuncture may be a suitable treatment option for regulating BP after stroke. However, the trials are not free from bias. Further reviews would yield positive results if well-designed trials are conducted.

Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph.

OBJECTIVE: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear. METHODS: The algorithm was developed using 6793 shoulder radiograph series performed between January 2015 and June 2018, which were labeled based on ultrasound or MRI conducted within 90 days, and clinical information (age, sex, dominant side, history of trauma, degree of pain). The output was the probability of significant rotator cuff tear (supraspinatus/infraspinatus complex tear with > 50% of tendon thickness). An operating point corresponding to sensitivity of 98% was set to achieve high negative predictive value (NPV) and low negative likelihood ratio (LR-). The performance of the algorithm was tested with 1095 radiograph series performed between July and December 2018. Subgroup analysis using Fisher's exact test was performed to identify factors (clinical information, radiography vendor, advanced imaging modality) associated with negative test results and NPV. RESULTS: Sensitivity, NPV, and LR- were 97.3%, 96.6%, and 0.06, respectively. The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients suspected of rotator cuff tear. The subgroup analysis showed that age < 60 years (p < 0.001), non-dominant side (p < 0.001), absence of trauma history (p = 0.001), and ultrasound examination (p < 0.001) were associated with negative test results. NPVs were higher in patients with age < 60 years (p = 0.024) and examined with ultrasound (p < 0.001). CONCLUSION: The deep learning algorithm could accurately rule out significant rotator cuff tear based on shoulder radiographs. KEY POINTS: • The deep learning algorithm can rule out significant rotator cuff tear with a negative likelihood ratio of 0.06 and a negative predictive value of 96.6%. • The deep learning algorithm can guide patients with significant rotator cuff tear to additional shoulder ultrasound or MRI with a sensitivity of 97.3%. • The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients with clinically suspected rotator cuff tear.

Herbal Medicine for Dumping Syndrome: A Systematic Review and Meta-Analysis.

Background: Dumping syndrome is a common complication of surgical treatment of gastric cancer, but conventional therapy has limitations related to symptom care due to its structural cause and the decreased quality of life. Objectives: The objective of this review was to assess the clinical evidence for the effectiveness of herbal medicine as a treatment for dumping syndrome. Methods: A literature review was conducted using 16 databases from their inceptions to March 2018. All randomized controlled trials (RCTs) of herbal medicine used to treat dumping syndrome patients were included and meta-analyzed. Methodological quality was assessed using the Cochrane Handbook for Systematic Reviews of Interventions. Results: A total of 174 dumping syndrome patients of 3 trials met all inclusion criteria. Two trials assessed the effectiveness of herbal medicine on the symptom response rate compared with conventional pharmacotherapy. Their results suggested significant effects in favor of herbal medicine (risk ratio [RR] = 1.37, 95% confidence interval [CI] = 1.16-1.63, P = .0003, heterogeneity τ2 = 0, χ2 = 0.02, P = .88, I2 = 0%). One trial assessed its effectiveness on the improvement rate of overall symptoms compared with conventional conservative complex therapy, such as postural management, diet regulation, and counseling (RR = 1.23, 95% CI = 0.96-1.58). Conclusions: Due to the small sample size, scarcity of reported articles, and lack of quality of the current RCTs, it was concluded that the effectiveness of herbal medicine in treating dumping syndrome is unclear.

Performance of a Deep Learning Algorithm in Detecting Osteonecrosis of the Femoral Head on Digital Radiography: A Comparison With Assessments by Radiologists.

OBJECTIVE. The objective of our study was to compare the sensitivity of a deep learning (DL) algorithm with the assessments by radiologists in diagnosing osteonecrosis of the femoral head (ONFH) using digital radiography. MATERIALS AND METHODS. We performed a two-center, retrospective, noninferiority study of consecutive patients (≥ 16 years old) with a diagnosis of ONFH based on MR images. We investigated the following four datasets of unilaterally cropped hip anteroposterior radiographs: training (n = 1346), internal validation (n = 148), temporal external test (n = 148), and geographic external test (n = 250). Diagnostic performance was measured for a DL algorithm, a less experienced radiologist, and an experienced radiologist. Noninferiority analyses for sensitivity were performed for the DL algorithm and both radiologists. Subgroup analysis for precollapse and postcollapse ONFH was done. RESULTS. Overall, 1892 hips (1037 diseased and 855 normal) were included. Sensitivity and specificity for the temporal external test set were 84.8% and 91.3% for the DL algorithm, 77.6% and 100.0% for the less experienced radiologist, and 82.4% and 100.0% for the experienced radiologist. Sensitivity and specificity for the geographic external test set were 75.2% and 97.2% for the DL algorithm, 77.6% and 75.0% for the less experienced radiologist, and 78.0% and 86.1% for the experienced radiologist. The sensitivity of the DL algorithm was noninferior to that of the assessments by both radiologists. The DL algorithm was more sensitive for precollapse ONFH than the assessment by the less experienced radiologist in the temporal external test set (75.9% vs 57.4%; 95% CI of the difference, 4.5-32.8%). CONCLUSION. The sensitivity of the DL algorithm for diagnosing ONFH using digital radiography was noninferior to that of both less experienced and experienced radiologist assessments.

Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography.

OBJECTIVES: The aim of this study was to compare the diagnostic performance of a deep learning algorithm with that of radiologists in diagnosing maxillary sinusitis on Waters' view radiographs. MATERIALS AND METHODS: Among 80,475 Waters' view radiographs, examined between May 2003 and February 2017, 9000 randomly selected cases were classified as normal or maxillary sinusitis based on radiographic findings and divided into training (n = 8000) and validation (n = 1000) sets to develop a deep learning algorithm. Two test sets composed of Waters' view radiographs with concurrent paranasal sinus computed tomography were labeled based on computed tomography findings: one with temporal separation (n = 140) and the other with geographic separation (n = 200) from the training set. Area under the receiver operating characteristics curve (AUC), sensitivity, and specificity of the algorithm and 5 radiologists were assessed. Interobserver agreement between the algorithm and majority decision of the radiologists was measured. The correlation coefficient between the predicted probability of the algorithm and average confidence level of the radiologists was determined. RESULTS: The AUCs of the deep learning algorithm were 0.93 and 0.88 for the temporal and geographic external test sets, respectively. The AUCs of the radiologists were 0.83 to 0.89 for the temporal and 0.75 to 0.84 for the geographic external test sets. The deep learning algorithm showed statistically significantly higher AUC than radiologist in both test sets. In terms of sensitivity and specificity, the deep learning algorithm was comparable to the radiologists. A strong interobserver agreement was noted between the algorithm and radiologists (Cohen κ coefficient, 0.82). The correlation coefficient between the predicted probability of the algorithm and confidence level of radiologists was 0.89 and 0.84 for the 2 test sets, respectively. CONCLUSIONS: The deep learning algorithm could diagnose maxillary sinusitis on Waters' view radiograph with superior AUC and comparable sensitivity and specificity to those of radiologists.

Herbal medicine for hemorrhage-related hydrocephalus: A systematic review of randomised controlled trials.

BACKGROUND & OBJECTIVE: Hemorrhage related hydrocephalus is a common complication intraventricular hemorrhage, subarachnoid hemorrhage, intracranial hemorrhage and traumatic brain injury. It increases morbidity and disability. The aim of this review is to assess the efficacy of herbal medicine for hemorrhage related hydrocephalus. METHODS: We searched randomized controlled trials (RCTs) investigating herbal medicine through 13 electronic databases. Extracting and assessing the data were performed independently by two authors. The Cochrane risk of bias tool was used to assess the risk of bias. RESULTS: We identified 15 trials investing 12 different herbal formulas. The methodological quality was generally low. In the 11 RCTs of comparisons with conventional treatment alone, herbal medicine plus conventional treatment showed a significant improvement on ventriculomegaly after treatment. Also, the 12 RCTs showed a significant difference on clinical signs and symptoms between two groups. We pooled the data of five trials compared Zhongfengxingnao liquid and Salviae Miltiorrhizae Radix plus conservative treatment with conservative treatment. There were significant effects on normalizing ventricle volume (Huckman index, RR 2.49; 95% CI 1.24-5.00, n = 80) and improving of Chinese scale of clinical neurologic deficit (MD -8.07; 95% CI -9.40 to -6.75, n = 264). CONCLUSION: Herbal medicine has potential benefits on improving ventriculomegaly and clinical signs and symptoms. However, a relatively small participants number and methodological limitations reduced the strength of the evidence. More rigorous trials are warranted.