Computer-aided detection in musculoskeletal projection radiography: A systematic review

This is the author accepted manuscript. The final version is available from WB Saunders via the DOI in this record.Objectives To investigated the accuracy of computer-aided detection (CAD) software in musculoskeletal projection radiography via a systematic review. Key findings Following selection screening, eligible studies were assessed for bias, and had their study characteristics extracted resulting in 22 studies being included. Of these 22 three studies had tested their CAD software in a clinical setting; the first study investigated vertebral fractures, reporting a sensitivity score of 69.3% with CAD, compared to 59.8% sensitivity without CAD. The second study tested dental caries diagnosis producing a sensitivity score of 68.8% and specificity of 94.1% with CAD, compared to sensitivity of 39.3% and specificity of 96.7% without CAD. The third indicated osteoporotic cases based on CAD, resulting in 100% sensitivity and 81.3% specificity. Conclusion The current evidence reported shows a lack of development into the clinical testing phase; however the research does show future promise in the variation of different CAD systems

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records

Improvement of region of interest extraction and scanning method of computer-aided diagnosis system for osteoporosis using panoramic radiographs

ObjectivesPatients undergoing osteoporosis treatment benefit greatly from early detection. We previously developed a computer-aided diagnosis (CAD) system to identify osteoporosis using panoramic radiographs. However, the region of interest (ROI) was relatively small, and the method to select suitable ROIs was labor-intensive. This study aimed to expand the ROI and perform semi-automatized extraction of ROIs. The diagnostic performance and operating time were also assessed.MethodsWe used panoramic radiographs and skeletal bone mineral density data of 200 postmenopausal women. Using the reference point that we defined by averaging 100 panoramic images as the lower mandibular border under the mental foramen, a 400x100-pixel ROI was automatically extracted and divided into four 100x100-pixel blocks. Valid blocks were analyzed using program 1, which examined each block separately, and program 2, which divided the blocks into smaller segments and performed scans/analyses across blocks. Diagnostic performance was evaluated using another set of 100 panoramic images.ResultsMost ROIs (97.0%) were correctly extracted. The operation time decreased to 51.4% for program 1 and to 69.3% for program 2. The sensitivity, specificity, and accuracy for identifying osteoporosis were 84.0, 68.0, and 72.0% for program 1 and 92.0, 62.7, and 70.0% for program 2, respectively. Compared with the previous conventional system, program 2 recorded a slightly higher sensitivity, although it occasionally also elicited false positives.ConclusionsPatients at risk for osteoporosis can be identified more rapidly using this new CAD system, which may contribute to earlier detection and intervention and improved medical care

GABUNGAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX DAN GRAY LEVEL RUN LENGTH MATRIX PADA ANALISIS CITRA RADIOGRAFI DENTAL PANORAMIC UNTUK DETEKSI DINI OSTEOPOROSIS

ABSTRAKOsteoporosis merupakan salah satu masalah kesehatan utama. Osteoporosis dianggap sebagai penyakit metabolik yang umum, dan sering diabaikan. Penyakit ini kebanyakan menyerang wanita dewasa yang dapat menyebabkan  kekurusan dan kerapuhan tulang, dan memicu patah tulang. Osteoporosis didiagnosis dengan mengukur Densitas Mineral Tulang menggunakan DXA (dual energy X-ray absorptiometry). Perawatan dengan alat ini membutuhkan biaya yang mahal, dan alat ini tidak tersedia secara luas. Sampel penelitian ini mengambil 19 orang dengan kriteria inklusi perempuan telah menopause, dinyatakan sehat, tidak mengalami patah tulang dan tidak memiliki kelainan tulang sejak lahir. Sampel diukur nilai bone mineral density (BMD) atau derajat osteoporosis dengan menggunakan DXA. Kemudian dilakukan pemotretan radiografi untuk mendapatkan citra dental panoramic. Tahapan penelitian adalah: 1) melakukan pre-processing terhadap citra radiografi panoramic tulang mandibular; 2) menentukan nilai tekstur citra metode  gray level co-occurrence matrix 3) menentukan nilai tekstur citra metode  gray level run length matrix 4) mengkalisifikasikan menggunakan metode k means kluster. Hasil Klasifikasi dengan menggunakan k means Kluster menunjukkan ketepatan klasifikasi sebesar 89,47% Kata kunci: radiografi; citra tulang rahang; BMD; analisis tekstur. ABSTRACTOsteoporosis is one of the major health problems. Osteoporosis is considered a common metabolic disease, and is often overlooked. This disease mostly affects adult women which can cause thin and brittle bones, and trigger fractures. Osteoporosis is diagnosed by measuring Bone Mineral Density using DXA (dual energy X-ray absorptiometry). Treatment with this device is expensive, and it is not widely available. The sample of this study took 19 people with the inclusion criteria of women having menopause, declared healthy, had no fractures and had no bone abnormalities since birth. The sample was measured the value of bone mineral density (BMD) or the degree of osteoporosis using DXA. Then, radiography was taken to obtain a panoramic dental image. The stages of the research are: 1) pre-processing the panoramic radiographic image of the mandible; 2) determine the texture value of the image using the gray level co-occurrence matrix method 3) determine the texture value of the image using the gray level run length matrix method 4) classify it using the k means cluster method.Classification results using k means clusters show the classification accuracy of 89.47% Keywords:. Radiography; dental panoramic; BMD; texture analysi