Automated detection of patellofemoral osteoarthritis from knee lateral view radiographs using deep learning : data from the Multicenter Osteoarthritis Study (MOST)

AbstractObjective: To assess the ability of imaging-based deep learning to detect radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs.Design: Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 18,436 knees). Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA. Patellar ROI was detected using deep-learning-based object detection method. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of classification models was assessed using the area under the receiver operating characteristic curve (ROC AUC) and the average precision (AP) obtained from the Precision-Recall (PR) curve in the stratified 5-fold cross validation setting.Results: Of the 18,436 knees, 3,425 (19%) had PFOA. AUC and AP for the reference model including age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren–Lawrence (KL) grade to detect PFOA were 0.806 and 0.478, respectively. The CNN model that used only image data significantly improved the classifier performance (ROC AUC = 0.958, AP = 0.862).Conclusion: We present the first machine learning based automatic PFOA detection method. Furthermore, our deep learning based model trained on patella region from knee lateral view radiographs performs better at detecting PFOA than models based on patient characteristics and clinical assessments.Abstract Objective: To assess the ability of imaging-based deep learning to detect radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 18,436 knees). Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA. Patellar ROI was detected using deep-learning-based object detection method. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of classification models was assessed using the area under the receiver operating characteristic curve (ROC AUC) and the average precision (AP) obtained from the Precision-Recall (PR) curve in the stratified 5-fold cross validation setting. Results: Of the 18,436 knees, 3,425 (19%) had PFOA. AUC and AP for the reference model including age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren–Lawrence (KL) grade to detect PFOA were 0.806 and 0.478, respectively. The CNN model that used only image data significantly improved the classifier performance (ROC AUC = 0.958, AP = 0.862). Conclusion: We present the first machine learning based automatic PFOA detection method. Furthermore, our deep learning based model trained on patella region from knee lateral view radiographs performs better at detecting PFOA than models based on patient characteristics and clinical assessments

Autologous microsurgical breast reconstruction and coronary artery bypass grafting: an anatomical study and clinical implications

OBJECTIVE: To identify possible avenues of sparing the internal mammary artery (IMA) for coronary artery bypass grafting (CABG) in women undergoing autologous breast reconstruction with deep inferior epigastric artery perforator (DIEP) flaps. BACKGROUND: Optimal autologous reconstruction of the breast and coronary artery bypass grafting (CABG) are often mutually exclusive as they both require utilisation of the IMA as the preferred arterial conduit. Given the prevalence of both breast cancer and coronary artery disease, this is an important issue for women's health as women with DIEP flap reconstructions and women at increased risk of developing coronary artery disease are potentially restricted from receiving this reconstructive option should the other condition arise. METHODS: The largest clinical and cadaveric anatomical study (n=315) to date was performed, investigating four solutions to this predicament by correlating the precise requirements of breast reconstruction and CABG against the anatomical features of the in situ IMAs. This information was supplemented by a thorough literature review. RESULTS: Minimum lengths of the left and right IMA needed for grafting to the left-anterior descending artery are 160.08 and 177.80 mm, respectively. Based on anatomical findings, the suitable options for anastomosis to each intercostals space are offered. In addition, 87-91% of patients have IMA perforator vessels to which DIEP flaps can be anastomosed in the first- and second-intercostal spaces. CONCLUSION: We outline five methods of preserving the IMA for future CABG: (1) lowering the level of DIEP flaps to the fourth- and fifth-intercostals spaces, (2) using the DIEP pedicle as an intermediary for CABG, (3) using IMA perforators to spare the IMA proper, (4) using and end-to-side anastomosis between the DIEP pedicle and IMA and (5) anastomosis of DIEP flaps using retrograde flow from the distal IMA. With careful patient selection, we hypothesize using the IMA for autologous breast reconstruction need not be an absolute contraindication for future CABG

<bold>The efficiency of electrocoagulation using aluminum electrodes</bold>i<bold>n treating wastewater from a dairy industry</bold>

This research deals with the investigation of electrocoagulation (EC) treatment of wastewater from a dairy plant using aluminum electrodes. Electrolysis time, pH, current density and distance between electrodes were considered to assess the removal efficiency of chemical oxygen demand (COD), total solids (TS) and their fractions and turbidity. Samples were collected from the effluent of a dairy plant using a sampling methodology proportional to the flow. The treatments were applied according to design factorial of half fraction with two levels of treatments and 3 repetitions at the central point. The optimization of parameters for treating dairy industry effluent by electrocoagulation using aluminum electrodes showed that electric current application for 21 minutes, an initial sample pH near 5.0 and a current density of 61.6A m-2 resulted in a significant reduction in COD by 57%; removal of turbidity by 99%, removal of total suspended solids by 92% and volatile suspended solids by 97%; and a final treated effluent pH of approximately 10. Optimum operating condition was used for cost calculations show that operating cost is approximately 3.48R$ m-3.</p

The KNee OsteoArthritis prediction (KNOAP2020) challenge : An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

AbstractObjectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth.Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC).Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables.Conclusions: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.Abstract Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusions: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation

In situ functionalization of a cellulosic-based activated carbon with magnetic iron oxides for the removal of carbamazepine from wastewater

The main goal of this work was to produce an easily recoverable waste-based magnetic activated carbon (MAC) for an efficient removal of the antiepileptic pharmaceutical carbamazepine (CBZ) from wastewater. For this purpose, the synthesis procedure was optimized and a material (MAC4) providing immediate recuperation from solution, remarkable adsorptive performance and relevant properties (specific surface area of 551 m2 g-1 and saturation magnetization of 39.84 emu g-1) was selected for further CBZ kinetic and equilibrium adsorption studies. MAC4 presented fast CBZ adsorption rates and short equilibrium times (< 30-45 min) in both ultrapure water and wastewater. Equilibrium studies showed that MAC4 attained maximum adsorption capacities (qm) of 68 ± 4 mg g-1 in ultrapure water and 60 ± 3 mg g-1 in wastewater, suggesting no significant interference of the aqueous matrix in the adsorption process. Overall, this work provides evidence of potential application of a waste-based MAC in the tertiary treatment of wastewaters.publishe

The KNee OsteoArthritis Prediction (KNOAP2020) challenge:An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusion: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.</p