Introduction: Ultrasound (US) is widely used for diagnosing liver disease, particularly cirrhosis, with key signs including liver shape irregularity and echostructure. The classification of liver parenchyma as smooth or coarse, indicative of chronic liver disease, is subjective and dependent on operator experience. To address this, we introduce LivGuard, a deep learning binary classifier designed to…
Madrid, September 12, 2024. Spain has unique conditions for the development of innovative medicines and a leading role, not only in Europe but globally, due to the excellence of its healthcare system, the ability to recruit patients, and the quality of the data generated by clinical trials.” This description by Luis Nudelman, Medical Director of…
COVID-19 detector: Our tool uses a deep convolutional network to detect and locate radiological signs associated with COVID-19 in frontal chest radiographs, differentiating them from those associated with common bacterial and viral pneumonias. The physician supplies the anonymized radiological image via a cell phone, website, or email to the cloud. The system also generates a heatmap indicating the region of the image where the algorithm detects radiological signs associated with this pathology.
Mammogram Analyzer: Mammograms can be difficult to interpret as the precision of the procedure depends in part on the technique used and the experience and skills of the radiologist. Topazium has designed and implemented a tailor-designed deep convolutional network on the task of detection and localization of abnormal radiological signs on mammograms. Our AI-based assistance tool could improve the overall accuracy of mammography reading and reducing the radiologists’ workload. It would also help in releasing the results of mammograms on patients’ schedules rather than those of radiology departments.
Pneumothorax localizer: Clinical suspicion of pneumothorax is usually confirmed by a radiologist based on a chest X-ray, a procedure which could be challenging if performed by professionals without extensive training. Topazium has implemented a framework that can detect and localize a pneumothorax in a given chest X-ray unsupervisely.
Global Analyzer: Chest radiography is one of the most commonly performed diagnostic examination for cardiovascular and respiratory life-threatening diseases. Engineered on the basis of deep neural networks, Topazium implemented a platform which detect and localize the presence of 16 radiological signs indicative of cardiovascular or respiratory clinical conditions on a frontal chest radiography faster than current clinical practice. The system can reduce time to diagnosis as well as fatigue-based diagnostic errors. It can also increase access to medical imaging expertise in parts of the world where interaction with skilled radiologists is limited.
Tuberculosis detector: Although chest X-rays provide important clues to tuberculosis early diagnosis, there are no radiological features which are in themselves pathognomonic of the infection, adding an additional difficulty for non-trained practitioners on detecting the disease. Topazium’s system could aid radiologists or other health care staff reading radiographic films, in tuberculosis systematic screening, infection control, and reduction of the cost of case detection within triage algorithms.
Patient-Drug Mapper: A significant number of hi-potential drugs end up falling through the cracks given their testing on patients with incompatible individual profiles. Topazium’s analytics allow the mapping of genetic and clinical patient’s conditions to a whole universe of experimental and approved drugs. Also, it can be used to produce a rank of matching alternative drugs given a patient’s profile, aiming both to increasing treatment efficiency whilst allowing drug re-profiling of vintage failed projects.
Pediatric Bone-Age Estimator: Calculating bone age through traditional procedures based on left wrist, hand, and fingers X-rays could be time-consuming and cumbersome. Topazium’s solution to this challenge is an algorithmic framework which instantly estimates the subject’s age by processing the aforementioned X-ray. Topazium’s system assess skeletal maturity with a mean absolute error of around 4 months, which is below the figure observed among practitioners.
Diabetic retinopathy (DR) can affect anyone with hyperglycemia, though it primarily impacts people who have had diabetes for many years. It can cause permanent blindness if left undiagnosed and untreated. Clinicians can identify DR by ophthalmoscopy or retinopathy. While these approaches are effective, they require a high degree of expertise. Topazium has developed a framework that can automatically classify ocular fundus images according to the various severity degrees of DR.
Lymph Node Metastasis Detector: Defining the presence or absence of lymph node metastases is essential in cancer diagnostics and staging, as it affects treatment and prognosis. This task is highly relevant but requires large amounts of reading time from a pathologist on-site or close to the point-of-care to analyze the samples. TOPAZIUM’s framework can detect cancer metastases in lymph nodes with an accuracy similar to the efficiency reported in the literature by expert pathologists.
Pigmented-Skin Lesions Classifier: Diagnosis in dermatology is largely based on visual inspection of a lesion or the suspicious skin area, depending largely on the experience and training of the dermatologist (or, general practitioners in areas where dermatological services are not readily available). Topazium’s framework currently classifies nine common pigmented-skin lesions with an accuracy similar to the efficiency reported in the literature by well-trained dermatologists.
Pneumothorax Diagnostician: Clinical suspicion of pneumothorax is usually confirmed by a radiologist based on a chest X-ray, a procedure which could be challenging if performed by professionals without extensive training. Topazium has implemented a framework that can detect and localize a pneumothorax in a given chest X-ray unsupervisely.
Melanoma: Diagnosis in dermatology is largely based on visual inspection of a lesion or the suspicious skin area, depending largely on the experience and training of the dermatologist (or, general practitioners in areas where dermatological services are not readily available). Topazium’s framework currently classifies seven common pigmented-skin lesions with an accuracy similar to the efficiency reported in the literature by well-trained dermatologists.
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Biological Age Predictor: Topazium has designed and implemented a framework to accurately estimate a subject’s biological age from routine laboratory tests’ outputs. Additionally, it identifies the key metrics responsible for the potential basis between biological and chronological age, allowing professionals to prescribe rectifying actions aiming to close the referred biological/chronological age gap.