A deep learning framework for automated detection and quantitative assessment of liver trauma.

BACKGROUND: Both early detection and severity assessment of liver trauma are critical for optimal triage and management of trauma patients. Current trauma protocols utilize computed tomography (CT) assessment of injuries in a subjective and qualitative (v.s. quantitative) fashion, shortcomings which could both be addressed by automated computer-aided systems that are capable of generating real-time reproducible and quantitative information. This study outlines an end-to-end pipeline to calculate the percentage of the liver parenchyma disrupted by trauma, an important component of the American Association for the Surgery of Trauma (AAST) liver injury scale, the primary tool to assess liver trauma severity at CT. METHODS: This framework comprises deep convolutional neural networks that first generate initial masks of both liver parenchyma (including normal and affected liver) and regions affected by trauma using three dimensional contrast-enhanced CT scans. Next, during the post-processing step, human domain knowledge about the location and intensity distribution of liver trauma is integrated into the model to avoid false positive regions. After generating the liver parenchyma and trauma masks, the corresponding volumes are calculated. Liver parenchymal disruption is then computed as the volume of the liver parenchyma that is disrupted by trauma. RESULTS: The proposed model was trained and validated on an internal dataset from the University of Michigan Health System (UMHS) including 77 CT scans (34 with and 43 without liver parenchymal trauma). The Dice/recall/precision coefficients of the proposed segmentation models are 96.13/96.00/96.35% and 51.21/53.20/56.76%, respectively, in segmenting liver parenchyma and liver trauma regions. In volume-based severity analysis, the proposed model yields a linear regression relation of 0.95 in estimating the percentage of liver parenchyma disrupted by trauma. The model shows an accurate performance in avoiding false positives for patients without any liver parenchymal trauma. These results indicate that the model is generalizable on patients with pre-existing liver conditions, including fatty livers and congestive hepatopathy. CONCLUSION: The proposed algorithms are able to accurately segment the liver and the regions affected by trauma. This pipeline demonstrates an accurate performance in estimating the percentage of liver parenchyma that is affected by trauma. Such a system can aid critical care medical personnel by providing a reproducible quantitative assessment of liver trauma as an alternative to the sometimes subjective AAST grading system that is used currently.

Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features.

BACKGROUND: Automated segmentation of coronary arteries is a crucial step for computer-aided coronary artery disease (CAD) diagnosis and treatment planning. Correct delineation of the coronary artery is challenging in X-ray coronary angiography (XCA) due to the low signal-to-noise ratio and confounding background structures. METHODS: A novel ensemble framework for coronary artery segmentation in XCA images is proposed, which utilizes deep learning and filter-based features to construct models using the gradient boosting decision tree (GBDT) and deep forest classifiers. The proposed method was trained and tested on 130 XCA images. For each pixel of interest in the XCA images, a 37-dimensional feature vector was constructed based on (1) the statistics of multi-scale filtering responses in the morphological, spatial, and frequency domains; and (2) the feature maps obtained from trained deep neural networks. The performance of these models was compared with those of common deep neural networks on metrics including precision, sensitivity, specificity, F1 score, AUROC (the area under the receiver operating characteristic curve), and IoU (intersection over union). RESULTS: With hybrid under-sampling methods, the best performing GBDT model achieved a mean F1 score of 0.874, AUROC of 0.947, sensitivity of 0.902, and specificity of 0.992; while the best performing deep forest model obtained a mean F1 score of 0.867, AUROC of 0.95, sensitivity of 0.867, and specificity of 0.993. Compared with the evaluated deep neural networks, both models had better or comparable performance for all evaluated metrics with lower standard deviations over the test images. CONCLUSIONS: The proposed feature-based ensemble method outperformed common deep convolutional neural networks in most performance metrics while yielding more consistent results. Such a method can be used to facilitate the assessment of stenosis and improve the quality of care in patients with CAD.

Clinical decision support systems in orthodontics: A narrative review of data science approaches.

Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.

Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications.

With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.

Adaptive Real-Time Removal of Impulse Noise in Medical Images.

Noise is an important factor that degrades the quality of medical images. Impulse noise is a common noise caused by malfunctioning of sensor elements or errors in the transmission of images. In medical images due to presence of white foreground and black background, many pixels have intensities similar to impulse noise and hence the distinction between noisy and regular pixels is difficult. Therefore, it is important to design a method to accurately remove this type of noise. In addition to the accuracy, the complexity of the method is very important in terms of hardware implementation. In this paper a low complexity de-noising method is proposed that distinguishes between noisy and non-noisy pixels and removes the noise by local analysis of the image blocks. All steps are designed to have low hardware complexity. Simulation results show that in the case of magnetic resonance images, the proposed method removes impulse noise with an acceptable accuracy.

Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units.

Intensive Care Units (ICUs) are equipped with many sophisticated sensors and monitoring devices to provide the highest quality of care for critically ill patients. However, these devices might generate false alarms that reduce standard of care and result in desensitization of caregivers to alarms. Therefore, reducing the number of false alarms is of great importance. Many approaches such as signal processing and machine learning, and designing more accurate sensors have been developed for this purpose. However, the significant intrinsic correlation among the extracted features from different sensors has been mostly overlooked. A majority of current data mining techniques fail to capture such correlation among the collected signals from different sensors that limits their alarm recognition capabilities. Here, we propose a novel information-theoretic predictive modeling technique based on the idea of coalition game theory to enhance the accuracy of false alarm detection in ICUs by accounting for the synergistic power of signal attributes in the feature selection stage. This approach brings together techniques from information theory and game theory to account for inter-features mutual information in determining the most correlated predictors with respect to false alarm by calculating Banzhaf power of each feature. The numerical results show that the proposed method can enhance classification accuracy and improve the area under the ROC (receiver operating characteristic) curve compared to other feature selection techniques, when integrated in classifiers such as Bayes-Net that consider inter-features dependencies.

Transcranial Color-Coded Sonography With Angle Correction As a Screening Tool for Raised Intracranial Pressure.

OBJECTIVES: Transcranial Doppler (TCD) has been evaluated as a noninvasive intracranial pressure (ICP) assessment tool. Correction for insonation angle, a potential source of error, with transcranial color-coded sonography (TCCS) has not previously been reported while evaluating ICP with TCD. Our objective was to study the accuracy of TCCS for detection of ICP elevation, with and without the use of angle correction. DESIGN: Prospective study of diagnostic accuracy. SETTING: Academic neurocritical care unit. PATIENTS: Consecutive adults with invasive ICP monitors. INTERVENTIONS: Ultrasound assessment with TCCS. MEASUREMENTS AND MAIN RESULTS: End-diastolic velocity (EDV), time-averaged peak velocity (TAPV), and pulsatility index (PI) were measured in the bilateral middle cerebral arteries with and without angle correction. Concomitant mean arterial pressure (MAP) and ICP were recorded. Estimated cerebral perfusion pressure (CPP) was calculated as estimated CPP (CPPe) = MAP × (EDV/TAPV) + 14, and estimated ICP (ICPe) = MAP-CPPe. Sixty patients were enrolled and 55 underwent TCCS. Receiver operating characteristic curve analysis of ICPe for detection of invasive ICP greater than 22 mm Hg revealed area under the curve (AUC) 0.51 (0.37-0.64) without angle correction and 0.73 (0.58-0.84) with angle correction. The optimal threshold without angle correction was ICPe greater than 18 mm Hg with sensitivity 71% (29-96%) and specificity 28% (16-43%). With angle correction, the optimal threshold was ICPe greater than 21 mm Hg with sensitivity 100% (54-100%) and specificity 30% (17-46%). The AUC for PI was 0.61 (0.47-0.74) without angle correction and 0.70 (0.55-0.92) with angle correction. CONCLUSIONS: Angle correction improved the accuracy of TCCS for detection of elevated ICP. Sensitivity was high, as appropriate for a screening tool, but specificity remained low.

Integrative Risk Predictors of Temporomandibular Joint Osteoarthritis Progression.

In this paper we propose feature selection and machine learning approaches to identify a combination of features for risk prediction of Temporomandibular Joint (TMJ) disease progression. In a sample of 32 TMJ osteoarthritis and 38 controls, feature selection of 5 clinical comorbidities, 43 quantitative imaging, 28 biological features and was performed using Maximum Relevance Minimum Redundancy, Chi-Square and Least Absolute Shrinkage and Selection Operator (LASSO) and Recursive Feature Elimination. We compared the performance of learning using concave and convex kernels (LUCCK), Support Vector Machine (SVM) and Random Forest (RF) approaches to predict disease cure/improvement or persistence/worsening. We show that the SVM model using LASSO achieves area under the curve (AUC), sensitivity and precision of 0.92±0.08, 0.85±0.19 and 0.76 ±0.18, respectively. Baseline levels of headaches, lower back pain, restless sleep, muscle soreness, articular fossa bone surface/bone volume and trabecular separation, condylar High Gray Level Run Emphasis and Short Run High Gray Level Emphasis, saliva levels of 6Ckine, Osteoprotegerin (OPG) and Angiogenin, and serum levels of 6ckine and Brain Derived Neurotrophic Factor (BDNF) were the most frequently occurring features to predict more severe TMJ osteoarthritis prognosis.

Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models Using Biological Privileged Information.

This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.

Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR.

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.

Novel Algorithm for Automated Optic Nerve Sheath Diameter Measurement Using a Clustering Approach.

INTRODUCTION: Using ultrasound to measure optic nerve sheath diameter (ONSD) has been shown to be a useful modality to detect elevated intracranial pressure. However, manual assessment of ONSD by a human operator is cumbersome and prone to human errors. We aimed to develop and test an automated algorithm for ONSD measurement using ultrasound images and compare it to measurements performed by physicians. MATERIALS AND METHODS: Patients were recruited from the Neurological Intensive Care Unit. Ultrasound images of the optic nerve sheath from both eyes were obtained using an ultrasound unit with an ocular preset. Images were processed by two attending physicians to calculate ONSD manually. The images were processed as well using a novel computerized algorithm that automatically analyzes ultrasound images and calculates ONSD. Algorithm-measured ONSD was compared with manually measured ONSD using multiple statistical measures. RESULTS: Forty-four patients with an average/Standard Deviation (SD) intracranial pressure of 14 (9.7) mmHg were recruited and tested (with a range between 1 and 57 mmHg). A t-test showed no statistical difference between the ONSD from left and right eyes (P > 0.05). Furthermore, a paired t-test showed no significant difference between the manually and algorithm-measured ONSD with a mean difference (SD) of 0.012 (0.046) cm (P > 0.05) and percentage error of difference of 6.43% (P = 0.15). Agreement between the two operators was highly correlated (interclass correlation coefficient = 0.8, P = 0.26). Bland-Altman analysis revealed mean difference (SD) of 0.012 (0.046) (P = 0.303) and limits of agreement between -0.1 and 0.08. Receiver Operator Curve analysis yielded an area under the curve of 0.965 (P < 0.0001) with high sensitivity and specificity. CONCLUSION: The automated image-analysis algorithm calculates ONSD reliably and with high precision when compared to measurements obtained by expert physicians. The algorithm may have a role in computer-aided decision support systems in acute brain injury.

Evaluating treatment-specific post-discharge quality-of-life and cost-effectiveness of TAVR and SAVR: Current practice & future directions.

BACKGROUND: Aortic stenosis is a prevalent valvular heart disease that is treated primarily by surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR), which are common treatments for addressing symptoms secondary to valvular heart disease. This narrative review article focuses on the existing literature comparing recovery and cost-effectiveness for SAVR and TAVR. METHODS: Major databases were searched for relevant literature discussing HRQOL and cost-effectiveness of TAVR and SAVR. We also searched for studies analyzing the use of wearable devices to monitor post-discharge recovery patterns. RESULTS: The literature focusing on quality-of-life following TAVR and SAVR has been limited primarily to single-center observational studies and randomized controlled trials. Studies focused on TAVR report consistent and rapid improvement relative to baseline status. Common HRQOL instruments (SF-36, EQ-5D, KCCQ, MLHFQ) have been used to document that TF-TAVR is advantageous over SAVR at 1-month follow-up, with the benefits leveling off following 1 year. TF-TAVR is economically favorable relative to SAVR, with estimated incremental cost-effectiveness ratio values ranging from $50,000 to $63,000/QALY gained. TA-TAVR has not been reported to be advantageous from an HRQOL or cost-effectiveness perspective. CONCLUSIONS: While real-world experiences are less described, large-scale trials have advanced our understanding of recovery and cost-effectiveness of aortic valve replacement treatment strategies. Future work should focus on scalable wearable device technology, such as smartwatches and heart-rate monitors, to facilitate real-world evaluation of TAVR and SAVR to support clinical decision-making and outcomes ascertainment.

Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers.

Diagnosis of temporomandibular joint (TMJ) Osteoarthritis (OA) before serious degradation of cartilage and subchondral bone occurs can help prevent chronic pain and disability. Clinical, radiomic, and protein markers collected from TMJ OA patients have been shown to be predictive of OA onset. Since protein data can often be unavailable for clinical diagnosis, we harnessed the learning using privileged information (LUPI) paradigm to make use of protein markers only during classifier training. Three different LUPI algorithms are compared with traditional machine learning models on a dataset extracted from 92 unique OA patients and controls. The best classifier performance of 0.80 AUC and 75.6 accuracy was obtained from the KRVFL+ model using privileged protein features. Results show that LUPI-based algorithms using privileged protein data can improve final diagnostic performance of TMJ OA classifiers without needing protein microarray data during classifier diagnosis.

Web Infrastructure for Data Management, Storage and Computation.

The Data Storage for Computation and Integration (DSCI) proposes management innovations for web-based secure data storage, algorithms deployment, and task execution. Its architecture allows inclusion of plugins for upload, browsing, sharing, and task execution in remote computing grids. Here, we demonstrate the DSCI implementation and the deployment of Image processing tools (TMJSeg), machine learning algorithms (MandSeg, DentalModelSeg), and advanced statistical packages (Multivariate Functional Shape Data Analysis, MFSDA), with data transfer and task execution handled by the clusterpost plug-in. Due to its comprehensive web-based design, local software installation is no longer required. The DSCI aims to enable and maintain a distributed computing and collaboration environment across multi-site clinical centers for the data processing of multisource features such as clinical, biological markers, volumetric images, and 3D surface models, with particular emphasis on analytics for temporomandibular joint osteoarthritis (TMJ OA).

Automatic Segmentation of Dental Root Canal and Merging with Crown Shape.

In this paper, machine learning approaches are proposed to support dental researchers and clinicians to study the shape and position of dental crowns and roots, by implementing a Patient Specific Classification and Prediction tool that includes RootCanalSeg and DentalModelSeg algorithms and then merges the output of these tools for intraoral scanning and volumetric dental imaging. RootCanalSeg combines image processing and machine learning approaches to automatically segment the root canals of the lower and upper jaws from large datasets, providing clinical information on tooth long axis for orthodontics, endodontics, prosthodontic and restorative dentistry procedures. DentalModelSeg includes segmenting the teeth from the crown shape to provide clinical information on each individual tooth. The merging algorithm then allows users to integrate dental models for quantitative assessments. Precision in dentistry has been mainly driven by dental crown surface characteristics, but information on tooth root morphology and position is important for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. In this paper we propose a patient specific classification and prediction of dental root canal and crown shape analysis workflow that employs image processing and machine learning methods to analyze crown surfaces, obtained by intraoral scanners, and three-dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography (CBCT).

Automatic Segmentation of Mandibular Ramus and Condyles.

In order to diagnose TMJ pathologies, we developed and tested a novel algorithm, MandSeg, that combines image processing and machine learning approaches for automatically segmenting the mandibular condyles and ramus. A deep neural network based on the U-Net architecture was trained for this task, using 109 cone-beam computed tomography (CBCT) scans. The ground truth label maps were manually segmented by clinicians. The U-Net takes 2D slices extracted from the 3D volumetric images. All the 3D scans were cropped depending on their size in order to keep only the mandibular region of interest. The same anatomic cropping region was used for every scan in the dataset. The scans were acquired at different centers with different resolutions. Therefore, we resized all scans to 512×512 in the pre-processing step where we also performed contrast adjustment as the original scans had low contrast. After the pre-processing, around 350 slices were extracted from each scan, and used to train the U-Net model. For the cross-validation, the dataset was divided into 10 folds. The training was performed with 60 epochs, a batch size of 8 and a learning rate of 2×10-5. The average performance of the models on the test set presented 0.95 ± 0.05 AUC, 0.93 ± 0.06 sensitivity, 0.9998 ± 0.0001 specificity, 0.9996 ± 0.0003 accuracy, and 0.91 ± 0.03 F1 score. This study findings suggest that fast and efficient CBCT image segmentation of the mandibular condyles and ramus from different clinical data sets and centers can be analyzed effectively. Future studies can now extract radiomic and imaging features as potentially relevant objective diagnostic criteria for TMJ pathologies, such as osteoarthritis (OA). The proposed segmentation will allow large datasets to be analyzed more efficiently for disease classification.

3D Auto-Segmentation of Mandibular Condyles.

Temporomandibular joints (TMJ) like a hinge connect the jawbone to the skull. TMJ disorders could cause pain in the jaw joint and the muscles controlling jaw movement. However, the disease cannot be diagnosed until it becomes symptomatic. It has been shown that bone resorption at the condyle articular surface is already evident at initial diagnosis of TMJ Osteoarthritis (OA). Therefore, analyzing the bone structure will facilitate the disease diagnosis. The important step towards this analysis is the condyle segmentation. This article deals with a method to automatically segment the temporomandibular joint condyle out of cone beam CT (CBCT) scans. In the proposed method we denoise images and apply 3D active contour and morphological operations to segment the condyle. The experimental results show that the proposed method yields the Dice score of 0.9461 with the standards deviation of 0.0888 when it is applied on CBCT images of 95 patients. This segmentation will allow large datasets to be analyzed more efficiently towards data sciences and machine learning approaches for disease classification.

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-ß1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.

Patient Specific Classification of Dental Root Canal and Crown Shape.

This paper proposes machine learning approaches to support dentistry researchers in the context of integrating imaging modalities to analyze the morphology of tooth crowns and roots. One of the challenges to jointly analyze crowns and roots with precision is that two different image modalities are needed. Precision in dentistry is mainly driven by dental crown surfaces characteristics, but information on tooth root shape and position is of great value for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. An innovative approach is to use image processing and machine learning to combine crown surfaces, obtained by intraoral scanners, with three dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography. In this paper, we propose a patient specific classification of dental root canal and crown shape analysis workflow that is widely applicable.

Automated Detection of Non-Informative Frames for Colonoscopy Through a Combination of Deep Learning and Feature Extraction.

Colonoscopy is a standard medical examination used to inspect the mucosal surface and detect abnormalities of the colon. Objective assessment and scoring of disease features in the colon are important in conditions such as colorectal cancer and inflammatory bowel disease. However, subjectivity in human disease assessment and measurement is hampered by interobserver variation and several biases. A computer-aided system for colonoscopy video analysis could facilitate diagnosis and disease severity measurement, which would aid in treatment selection and clinical outcome prediction. However, a large number of images captured during colonoscopy are non-informative, making detecting and removing those frames an important first step in performing automated analysis. In this paper, we present a combination of deep learning and conventional feature extraction to distinguish non-informative from informative images in patients with ulcerative colitis. Our result shows that the combination of bottleneck features in the RGB color space and hand-crafted features in the HSV color space can boost the classification performance. Our proposed method was validated using 5-fold cross-validation and achieved an average AUC of 0.939 and an average F1 score of 0.775.