Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5–5.2% worldwide. The traditional methods for scoliosis screening are easily accessible but require unnecessary referrals and radiography exposure due to their low positive predictive values. The application of deep learning algorithms has the potential to reduce unnecessary referrals and costs in scoliosis screening. Here, we developed and validated deep learning algorithms for automated scoliosis screening using unclothed back images. The accuracies of the algorithms were superior to those of human specialists in detecting scoliosis, detecting cases with a curve ≥20°, and severity grading for both binary classifications and the four-class classification. Our approach can be potentially applied in routine scoliosis screening and periodic follow-ups of pretreatment cases without radiation exposure.

The sensitivity of endoscopy in diagnosing chronic atrophic gastritis is only 42%, and multipoint biopsy, despite being more accurate, is not always available. This study aimed to construct a convolutional neural network to improve the diagnostic rate of chronic atrophic gastritis. We collected 5470 images of the gastric antrums of 1699 patients and labeled them with their pathological findings. Of these, 3042 images depicted atrophic gastritis and 2428 did not. We designed and trained a convolutional neural network-chronic atrophic gastritis model to diagnose atrophic gastritis accurately, verified by five-fold cross-validation. Moreover, the diagnoses of the deep learning model were compared with those of three experts. The diagnostic accuracy, sensitivity, and specificity of the convolutional neural network-chronic atrophic gastritis model in diagnosing atrophic gastritis were 0.942, 0.945, and 0.940, respectively, which were higher than those of the experts. The detection rates of mild, moderate, and severe atrophic gastritis were 93%, 95%, and 99%, respectively. Chronic atrophic gastritis could be diagnosed by gastroscopic images using the convolutional neural network-chronic atrophic gastritis model. This may greatly reduce the burden on endoscopy physicians, simplify diagnostic routines, and reduce costs for doctors and patients.

Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.

Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Although artificial intelligence performs promisingly in medicine, but there is few automatic disease diagnosis platforms which can clearly explain why a specific medical decision is made. Objective: The goal of this study is to devise and develop an interpretable and expandable diagnosis framework for automatically diagnosing multiple ocular diseases and providing treatment recommendations as the particular illness of specific patient. This system can simulate the complete diagnostic process of experienced doctors so that diagnostic process can be tracked and understood. Methods: The diagnosis of ocular diseases is highly dependent on observing medical images. Therefore, current research chose ophthalmic images as research material. All medical images were classified to different types of diseases or normal; each image is decomposed into different parts according to the anatomical knowledge and then annotated. This process yields the positions and primary information on the different anatomical parts and foci observed in medical images. Thus, the gap between medical image and diagnosis process can be bridged. Next, the diagnosis of other diseases can rely on the combination of different anatomical parts and foci. We applied images and the information produced during annotation process to implement an interpretable and expandable automatically diagnosis framework with deep learning. Results: This diagnosis framework consists of four stages. The first stage can identify the type of disease, and the identification accuracy reaches 93%. The second stage can localize the anatomical parts and foci of eye, the localization accuracy reaches 82% and 90% for images under natural light without fluorescein sodium eye drops and images under cobalt blue light or natural light with fluorescein sodium eye drops respectively. The third stage can carefully classify the specific condition of each anatomical part or focus with the result from the second stage, the average accuracy for multiple classification problems in this stage reaches 79% - 98%. The last stage is providing treatment advices according to medical experience and artificial intelligence which is merely involved with pterygium, its accuracy surpasses 95%. On this basis, we developed a telemedical system which can show detailed reasons for a particular diagnosis to doctors and patients to help doctors with medical decision making. This system can carefully analyze medical images and provide treatment advices according to the analysis results and consultation between doctor and patient. Conclusions: The interpretable and expandable medical artificial intelligence platform was successfully built. This system can identify the disease, distinguish different anatomical parts and foci, discern the diagnostic information that is relevant to the diagnosis of diseases and provide treatment suggestions. During this process, whole diagnostic flow becomes clear and understandable to both doctors and their patients. Moreover, other diseases can be seamlessly integrated into this system without any influence on existing modules or diseases. Furthermore, this framework can assist in the clinical training of junior doctor. Due to the rare high-grade medical resource, it is impossible that everyone receive professional diagnosis and treatment service with high quality. This framework can not only be applied in hospitals with insufficient medical resources to decrease the pressure on experienced doctors and deployed in remote areas to help doctors diagnose common ocular diseases.