Deep Learning Classifier with Patient's Metadata of Dermoscopic Images in Malignant Melanoma Detection

Multimodal convolutional neural network-based algorithm for real-time detection and differentiation of malignant and inflammatory biliary strictures in cholangioscopy: a proof-of-concept study (with video)

BACKGROUND AND AIMS

Deep learning algorithms gained attention for detection (computer-aided detection [CADe]) of biliary tract cancer in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for detection (CADe), characterization and discriminating (computer-aided diagnosis [CADx]) between malignant, inflammatory, and normal biliary tissue in raw dSOC videos. In addition, clinical metadata were included in the CNN algorithm to overcome limitations of image-only models.

METHODS

Based on dSOC videos and images of 111 patients (total of 15,158 still frames), a real-time CNN-based algorithm for CADe and CADx was developed and validated. We established an image-only model and metadata injection approach. In addition, frame-wise and case-based predictions on complete dSOC video sequences were validated. Model embeddings were visualized, and class activation maps highlighted relevant image regions.

RESULTS

The concatenation-based CADx approach achieved a per-frame area under the receiver-operating characteristic curve of .871, sensitivity of .809 (95% CI, .784-.832), specificity of .773 (95% CI, .761-.785), positive predictive value of .450 (95% CI, .423-.467), and negative predictive value of .946 (95% CI, .940-.954) with respect to malignancy on 5715 test frames from complete videos of 20 patients. For case-based diagnosis using average prediction scores, 6 of 8 malignant cases and all 12 benign cases were identified correctly.

CONCLUSIONS

Our algorithm distinguishes malignant and inflammatory bile duct lesions in dSOC videos, indicating the potential of CNN-based diagnostic support systems for both CADe and CADx. The integration of non-image data can improve CNN-based support systems, targeting current challenges in the assessment of biliary strictures.

A multi-stage multi-modal learning algorithm with adaptive multimodal fusion for improving multi-label skin lesion classification

Skin cancer is frequently occurring and has become a major contributor to both cancer incidence and mortality. Accurate and timely diagnosis of skin cancer holds the potential to save lives. Deep learning-based methods have demonstrated significant advancements in the screening of skin cancers. However, most current approaches rely on a single modality input for diagnosis, thereby missing out on valuable complementary information that could enhance accuracy. Although some multimodal-based methods exist, they often lack adaptability and fail to fully leverage multimodal information. In this paper, we introduce a novel uncertainty-based hybrid fusion strategy for a multi-modal learning algorithm aimed at skin cancer diagnosis. Our approach specifically combines three different modalities: clinical images, dermoscopy images, and metadata, to make the final classification. For the fusion of two image modalities, we employ an intermediate fusion strategy that considers the similarity between clinical and dermoscopy images to extract features containing both complementary and correlated information. To capture the correlated information, we utilize cosine similarity, and we employ concatenation as the means for integrating complementary information. In the fusion of image and metadata modalities, we leverage uncertainty to obtain confident late fusion results, allowing our method to adaptively combine the information from different modalities. We conducted comprehensive experiments using a popular publicly available skin disease diagnosis dataset, and the results of these experiments demonstrate the effectiveness of our proposed method. Our proposed fusion algorithm could enhance the clinical applicability of automated skin lesion classification, offering a more robust and adaptive way to make automatic diagnoses with the help of uncertainty mechanism. Code is available at https://github.com/Zuo-Lihan/CosCatNet-Adaptive_Fusion_Algorithm.

Analysis of the use of digital technologies in the preliminary diagnosis of dermatological diseases: a systematic review

Dermatological diseases are a significant global health concern, and advanced technologies have demonstrated considerable potential to improve the diagnosis and treatment of these conditions. The overall objective of this systematic review is to analyze and evaluate the use of preliminary digital diagnostic technologies in the field of dermatological diseases. The PRISMA methodology was used to collect approximately 50 products to support the article. The results obtained reveal several key findings. First, we investigate for which dermatological diseases these specialized technologies are used, finding that conditions such as skin cancer, rosacea and acne are the most diagnosed using advanced tools. Second, the technologies used to improve preliminary diagnosis are explored, with neural networks standing out, contributing to more accurate and efficient diagnosis. Third, the benefits of these technologies are evaluated, highlighting diagnostic accuracy, early detection and improved quality of patient care. In conclusion, this review highlights the crucial role of technologies in dermatology, not only improving diagnostic accuracy and treatment efficiency, but also optimizing resources and improving the patient experience.

Artificial Intelligence in the Non-Invasive Detection of Melanoma

Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.

Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review

The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies are improved. Currently, diagnosis is heavily based on physicians' judgment and experience, which can occasionally lead to the worsening of the lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy or multiphoton laser scanning microscopy, have been explored for skin cancer assessment, which have been aligned with different artificial intelligence (AI) strategies to assist in the diagnostic task, based on several image features, thus making the process more reliable and faster. This systematic review concerns the implementation of AI methods for skin tumor classification with different imaging modalities, following the PRISMA guidelines. In total, 206 records were retrieved and qualitatively analyzed. Diagnostic potential was found for several techniques, particularly for dermoscopy images, with strategies yielding classification results close to perfection. Learning approaches based on support vector machines and artificial neural networks seem to be preferred, with a recent focus on convolutional neural networks. Still, detailed descriptions of training/testing conditions are lacking in some reports, hampering reproduction. The use of AI methods in skin cancer diagnosis is an expanding field, with future work aiming to construct optimal learning approaches and strategies. Ultimately, early detection could be optimized, improving patient outcomes, even in areas where healthcare is scarce.

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects

In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.

Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present

There has been growing scientific interest in the research field of deep learning techniques applied to skin cancer diagnosis in the last decade. Though encouraging data have been globally reported, several discrepancies have been observed in terms of study methodology, result presentations and validation in clinical settings. The present review aimed to screen the scientific literature on the application of DL techniques to dermoscopic melanoma/nevi differential diagnosis and extrapolate those original studies adequately by reporting on a DL model, comparing them among clinicians and/or another DL architecture. The second aim was to examine those studies together according to a standard set of statistical measures, and the third was to provide dermatologists with a comprehensive explanation and definition of the most used artificial intelligence (AI) terms to better/further understand the scientific literature on this topic and, in parallel, to be updated on the newest applications in the medical dermatologic field, along with a historical perspective. After screening nearly 2000 records, a subset of 54 was selected. Comparing the 20 studies reporting on convolutional neural network (CNN)/deep convolutional neural network (DCNN) models, we have a scenario of highly performant DL algorithms, especially in terms of low false positive results, with average values of accuracy (83.99%), sensitivity (77.74%), and specificity (80.61%). Looking at the comparison with diagnoses by clinicians (13 studies), the main difference relies on the specificity values, with a +15.63% increase for the CNN/DCNN models (average specificity of 84.87%) compared to humans (average specificity of 64.24%) with a 14,85% gap in average accuracy; the sensitivity values were comparable (79.77% for DL and 79.78% for humans). To obtain higher diagnostic accuracy and feasibility in clinical practice, rather than in experimental retrospective settings, future DL models should be based on a large dataset integrating dermoscopic images with relevant clinical and anamnestic data that is prospectively tested and adequately compared with physicians.

Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata

BACKGROUND

Sexually transmitted infections (STIs) pose a significant global public health challenge. Early diagnosis and treatment reduce STI transmission, but rely on recognising symptoms and care-seeking behaviour of the individual. Digital health software that distinguishes STI skin conditions could improve health-seeking behaviour. We developed and evaluated a deep learning model to differentiate STIs from non-STIs based on clinical images and symptoms.

METHODS

We used 4913 clinical images of genital lesions and metadata from the Melbourne Sexual Health Centre collected during 2010-2023. We developed two binary classification models to distinguish STIs from non-STIs: (1) a convolutional neural network (CNN) using images only and (2) an integrated model combining both CNN and fully connected neural network (FCN) using images and metadata. We evaluated the model performance by the area under the ROC curve (AUC) and assessed metadata contributions to the Image-only model.

RESULTS

Our study included 1583 STI and 3330 non-STI images. Common STI diagnoses were syphilis (34.6%), genital warts (24.5%) and herpes (19.4%), while most non-STIs (80.3%) were conditions such as dermatitis, lichen sclerosis and balanitis. In both STI and non-STI groups, the most frequently observed groups were 25-34 years (48.6% and 38.2%, respectively) and heterosexual males (60.3% and 45.9%, respectively). The Image-only model showed a reasonable performance with an AUC of 0.859 (SD 0.013). The Image + Metadata model achieved a significantly higher AUC of 0.893 (SD 0.018) compared to the Image-only model (p < 0.01). Out of 21 metadata, the integration of demographic and dermatological metadata led to the most significant improvement in model performance, increasing AUC by 6.7% compared to the baseline Image-only model.

CONCLUSIONS

The Image + Metadata model outperformed the Image-only model in distinguishing STIs from other skin conditions. Using it as a screening tool in a clinical setting may require further development and evaluation with larger datasets.

Crow search algorithm with deep transfer learning driven skin lesion detection on dermoscopic images

The early diagnosis of skin cancer is of paramount importance for effective patient treatment. Dermoscopy, a non-surgical technique, utilizes precise equipment to examine the skin and plays a crucial role in identifying specific features and patterns that may indicate the presence of skin carcinoma. In recent times, machine learning (ML) methods have been developed to recognize and classify dermoscopic images as either malignant or benign. Deep learning (DL) systems, including Convolutional Neural Networks (CNNs), as well as various ML models like Random Forest (RF) classifiers and Support Vector Machine (SVM), are employed to extract relevant features from these images. This study introduces the Crow Search Algorithm with Deep Transfer Learning Driven Skin Lesion Detection on Dermoscopic Images (CSADTL-SLD) technique. The CSADTL-SLD method starts with the application of a median filter (MF) to remove noise from the images and utilizes the GoogleNet model for feature extraction. GoogleNet is well-regarded for its capacity to capture intricate and meaningful patterns within the data, which are essential for accurate lesion characterization. Furthermore, the CSADTL-SLD technique applies the Crow Search Algorithm (CSA) for parameter tuning of the GoogleNet model. After feature selection, the system employs the MLP classification model for precise lesion categorization. The comprehensive results of this research demonstrate the superiority of the CSADTL-SLD algorithm, showing significant enhancements in skin lesion detection accuracy and robustness when compared to existing methods. This approach holds promise as an effective solution for automating the detection and classification of skin lesions in dermoscopic images.

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan

BACKGROUND

Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing initial CT scans using a CNN-based algorithm.

METHODS

We conducted a retrospective multicentric study of a consecutive cohort of patients with SAH. Demographic, clinical and radiological variables were analyzed. Preprocessed baseline CT scan images were used as the input for training using the AUCMEDI framework. Our model's architecture leveraged a DenseNet121 structure, employing transfer learning principles. The output variable was mortality in the first three months.

RESULTS

Images from 219 patients were processed; 175 for training and validation and 44 for the model's evaluation. Of the patients, 52% (115/219) were female and the median age was 58 (SD = 13.06) years. In total, 18.5% (39/219) had idiopathic SAH. The mortality rate was 28.5% (63/219). The model showed good accuracy at predicting mortality in SAH patients when exclusively using the images of the initial CT scan (accuracy = 74%, F1 = 75% and AUC = 82%).

CONCLUSION

Modern image processing techniques based on AI and CNN make it possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients, resulting in better training, development and performance on tasks that are beyond the skills of conventional clinical knowledge.

Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification

A dermatologist-like automatic classification system is developed in this paper to recognize nine different classes of pigmented skin lesions (PSLs), using a separable vision transformer (SVT) technique to assist clinical experts in early skin cancer detection. In the past, researchers have developed a few systems to recognize nine classes of PSLs. However, they often require enormous computations to achieve high performance, which is burdensome to deploy on resource-constrained devices. In this paper, a new approach to designing SVT architecture is developed based on SqueezeNet and depthwise separable CNN models. The primary goal is to find a deep learning architecture with few parameters that has comparable accuracy to state-of-the-art (SOTA) architectures. This paper modifies the SqueezeNet design for improved runtime performance by utilizing depthwise separable convolutions rather than simple conventional units. To develop this Assist-Dermo system, a data augmentation technique is applied to control the PSL imbalance problem. Next, a pre-processing step is integrated to select the most dominant region and then enhance the lesion patterns in a perceptual-oriented color space. Afterwards, the Assist-Dermo system is designed to improve efficacy and performance with several layers and multiple filter sizes but fewer filters and parameters. For the training and evaluation of Assist-Dermo models, a set of PSL images is collected from different online data sources such as Ph2, ISBI-2017, HAM10000, and ISIC to recognize nine classes of PSLs. On the chosen dataset, it achieves an accuracy (ACC) of 95.6%, a sensitivity (SE) of 96.7%, a specificity (SP) of 95%, and an area under the curve (AUC) of 0.95. The experimental results show that the suggested Assist-Dermo technique outperformed SOTA algorithms when recognizing nine classes of PSLs. The Assist-Dermo system performed better than other competitive systems and can support dermatologists in the diagnosis of a wide variety of PSLs through dermoscopy. The Assist-Dermo model code is freely available on GitHub for the scientific community.

A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI

Skin cancer is a serious disease that affects people all over the world. Melanoma is an aggressive form of skin cancer, and early detection can significantly reduce human mortality. In the United States, approximately 97,610 new cases of melanoma will be diagnosed in 2023. However, challenges such as lesion irregularities, low-contrast lesions, intraclass color similarity, redundant features, and imbalanced datasets make improved recognition accuracy using computerized techniques extremely difficult. This work presented a new framework for skin lesion recognition using data augmentation, deep learning, and explainable artificial intelligence. In the proposed framework, data augmentation is performed at the initial step to increase the dataset size, and then two pretrained deep learning models are employed. Both models have been fine-tuned and trained using deep transfer learning. Both models (Xception and ShuffleNet) utilize the global average pooling layer for deep feature extraction. The analysis of this step shows that some important information is missing; therefore, we performed the fusion. After the fusion process, the computational time was increased; therefore, we developed an improved Butterfly Optimization Algorithm. Using this algorithm, only the best features are selected and classified using machine learning classifiers. In addition, a GradCAM-based visualization is performed to analyze the important region in the image. Two publicly available datasets-ISIC2018 and HAM10000-have been utilized and obtained improved accuracy of 99.3% and 91.5%, respectively. Comparing the proposed framework accuracy with state-of-the-art methods reveals improved and less computational time.

Salama A, Mustafa Hilal A, Alsaid MI, Osman AE, Alneil AA. Henry Gas Solubility Optimization Algorithm based Feature Extraction in Dermoscopic Images Analysis of Skin Cancer

Artificial Intelligence (AI) techniques have changed the general perceptions about medical diagnostics, especially after the introduction and development of Convolutional Neural Networks (CNN) and advanced Deep Learning (DL) and Machine Learning (ML) approaches. In general, dermatologists visually inspect the images and assess the morphological variables such as borders, colors, and shapes to diagnose the disease. In this background, AI techniques make use of algorithms and computer systems to mimic the cognitive functions of the human brain and assist clinicians and researchers. In recent years, AI has been applied extensively in the domain of dermatology, especially for the detection and classification of skin cancer and other general skin diseases. In this research article, the authors propose an Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique for the detection of skin cancer in dermoscopic images. The primary aim of the proposed MAFCNN-SCD technique is to classify skin cancer on dermoscopic images. In the presented MAFCNN-SCD technique, the data pre-processing is performed at the initial stage. Next, the MAFNet method is applied as a feature extractor with Henry Gas Solubility Optimization (HGSO) algorithm as a hyperparameter optimizer. Finally, the Deep Belief Network (DBN) method is exploited for the detection and classification of skin cancer. A sequence of simulations was conducted to establish the superior performance of the proposed MAFCNN-SCD approach. The comprehensive comparative analysis outcomes confirmed the supreme performance of the proposed MAFCNN-SCD technique over other methodologies.

CROPro: a tool for automated cropping of prostate magnetic resonance images

Purpose

To bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images.

Approach

CROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.g., prostate) with different image sizes, pixel spacing, and sampling strategies. Performance was evaluated in the context of clinically significant prostate cancer (csPCa) classification. Transfer learning was used to train five convolutional neural network (CNN) and five vision transformer (ViT) models using different combinations of cropped image sizes ( 64 × 64 , 128 × 128 , and 256 × 256  pixels2), pixel spacing ( 0.2 × 0.2 , 0.3 × 0.3 , 0.4 × 0.4 , and 0.5 × 0.5    mm 2 ), and sampling strategies (center, random, and stride cropping) over the prostate. T2-weighted MR images ( N = 1475 ) from the online available PI-CAI challenge were used to train ( N = 1033 ), validate ( N = 221 ), and test ( N = 221 ) all models.

Results

Among CNNs, SqueezeNet with stride cropping (image size: 128 × 128 , pixel spacing: 0.2 × 0.2    mm 2 ) achieved the best classification performance ( 0.678 ± 0.006 ). Among ViTs, ViT-H/14 with random cropping (image size: 64 × 64 and pixel spacing: 0.5 × 0.5    mm 2 ) achieved the best performance ( 0.756 ± 0.009 ). Model performance depended on the cropped area, with optimal size generally larger with center cropping ( ∼ 40    cm 2 ) than random/stride cropping ( ∼ 10    cm 2 ).

Conclusion

We found that csPCa classification performance of CNNs and ViTs depends on the cropping settings. We demonstrated that CROPro is well suited to optimize these settings in a standardized manner, which could improve the overall performance of deep learning models.

Ensemble method using real images, metadata and synthetic images for control of class imbalance in classification

Binary classification and anomaly detection face the problem of class imbalance in data sets. The contribution of this paper is to provide an ensemble model that improves image binary classification by reducing the class imbalance between the minority and majority classes in a data set. The ensemble model is a classifier of real images, synthetic images, and metadata associated with the real images. First, we apply a generative model to synthesize images of the minority class from the real image data set. Secondly, we train the ensemble model jointly with synthesized images of the minority class, real images, and metadata. Finally, we evaluate the model performance using a sensitivity metric to observe the difference in classification resulting from the adjustment of class imbalance. Improving the imbalance of the minority class by adding half the size of the majority class we observe an improvement in the classifier’s sensitivity by 12% and 24% for the benchmark pre-trained models of RESNET50 and DENSENet121 respectively.

Management of cutaneous melanoma: radiologists challenging and risk assessment

Melanoma patient remains a challenging for the radiologist, due to the difficulty related to the management of a patient more often in an advanced stage of the disease. It is necessary to determine a stratification of risk, optimizing the means, with diagnostic tools that should be optimized in relation to the type of patient, and improving knowledge. Staging and risk assessment procedures are determined by disease presentation at diagnosis. Melanoma staging is a critical tool to assist clinical decision-making and prognostic assessment. It is used for clinical trial design, eligibility, stratification, and analysis. The current standard for regional lymph nodes staging is represented by the sentinel lymph node excision biopsy procedure. For staging of distant metastases, PET-CT has the highest sensitivity and diagnostic odds ratio. Similar trend is observed during melanoma surveillance. The advent of immunotherapy, which has improved patient outcome, however, has determined new issues for radiologists, partly due to atypical response patterns, partly due to adverse reactions that must be identified as soon as possible for the correct management of the patient. The main objectives of the new ir-criteria are to standardize the assessment between different trials. However, these ir-criteria do not take into account all cases of atypical response patterns, as hyperprogression or dissociated responses. None of these criteria has actually been uniformly adopted in routine. The immune-related adverse events (irAEs) can involve various organs from head to toe. It is crucial for radiologists to know the imaging appearances of this condition, to exclude recurrent or progressive disease and for pneumonitis, since it could be potentially life-threatening toxicity, resulting in pneumonitis-related deaths in early phase trials, to allow a proper patient management.

A multimodal transformer to fuse images and metadata for skin disease classification

Skin disease cases are rising in prevalence, and the diagnosis of skin diseases is always a challenging task in the clinic. Utilizing deep learning to diagnose skin diseases could help to meet these challenges. In this study, a novel neural network is proposed for the classification of skin diseases. Since the datasets for the research consist of skin disease images and clinical metadata, we propose a novel multimodal Transformer, which consists of two encoders for both images and metadata and one decoder to fuse the multimodal information. In the proposed network, a suitable Vision Transformer (ViT) model is utilized as the backbone to extract image deep features. As for metadata, they are regarded as labels and a new Soft Label Encoder (SLE) is designed to embed them. Furthermore, in the decoder part, a novel Mutual Attention (MA) block is proposed to better fuse image features and metadata features. To evaluate the model's effectiveness, extensive experiments have been conducted on the private skin disease dataset and the benchmark dataset ISIC 2018. Compared with state-of-the-art methods, the proposed model shows better performance and represents an advancement in skin disease diagnosis.

Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study

Background

Web-based computerized adaptive testing (CAT) implementation of the skin cancer (SC) risk scale could substantially reduce participant burden without compromising measurement precision. However, the CAT of SC classification has not been reported in academics thus far.

Objective

We aim to build a CAT-based model using machine learning to develop an app for automatic classification of SC to help patients assess the risk at an early stage.

Methods

We extracted data from a population-based Australian cohort study of SC risk (N=43,794) using the Rasch simulation scheme. All 30 feature items were calibrated using the Rasch partial credit model. A total of 1000 cases following a normal distribution (mean 0, SD 1) based on the item and threshold difficulties were simulated using three techniques of machine learning—naïve Bayes, k-nearest neighbors, and logistic regression—to compare the model accuracy in training and testing data sets with a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, receiver operating characteristic curve (area under the curve [AUC]), and CIs along with the accuracy and precision across the proposed models for comparison. An app that classifies the SC risk of the respondent was developed.

Results

We observed that the 30-item k-nearest neighbors model yielded higher AUC values of 99% and 91% for the 700 training and 300 testing cases, respectively, than its 2 counterparts using the hold-out validation but had lower AUC values of 85% (95% CI 83%-87%) in the k-fold cross-validation and that an app that predicts SC classification for patients was successfully developed and demonstrated in this study.

Conclusions

The 30-item SC prediction model, combined with the Rasch web-based CAT, is recommended for classifying SC in patients. An app we developed to help patients self-assess SC risk at an early stage is required for application in the future.

A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record

PURPOSE

To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data.

PATIENTS AND METHODS

We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection.

RESULTS

This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (-0.76% loss) and 0.9644 (-0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features.

CONCLUSION

Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.

Systemic retinal biomarkers

PURPOSE OF REVIEW

Systemic retinal biomarkers are biomarkers identified in the retina and related to evaluation and management of systemic disease. This review summarizes the background, categories and key findings from this body of research as well as potential applications to clinical care.

RECENT FINDINGS

Potential systemic retinal biomarkers for cardiovascular disease, kidney disease and neurodegenerative disease were identified using regression analysis as well as more sophisticated image processing techniques. Deep learning techniques were used in a number of studies predicting diseases including anaemia and chronic kidney disease. A virtual coronary artery calcium score performed well against other competing traditional models of event prediction.

SUMMARY

Systemic retinal biomarker research has progressed rapidly using regression studies with clearly identified biomarkers such as retinal microvascular patterns, as well as using deep learning models. Future systemic retinal biomarker research may be able to boost performance using larger data sets, the addition of meta-data and higher resolution image inputs.