Posts in category: Blogs – English

For many people it remains unrecognized that even minor symptoms can indicate the development of dementia. They thus miss the important early diagnosis and the timely start of their individualized therapy and thus the best possible prognosis for many years. AI, together with brain MRI, can contribute to the earliest possible detection and treatment of neurodegenerative diseases.

It is with great pleasure that we present AIRAscore, an AI software for the precise diagnosis of neurodegenerative diseases.

What AIRAscore is and how it works

AIRAscore measures relevant biomarkers for the diagnosis and differential diagnosis of neurodegenerative diseases from MR images of the brain.

Global and regional brain volumes are accurately measured. The results are always compared with age- and gender-specific reference values. The measurements are many times more accurate than even specialists have been able to carry out in the past. Results that deviate from normal are clearly evident. The findings are reported in tabular form in bar graphs and support the knowledge transfer from radiologists to patients and physicians.

Who benefits

Patients, clinicians and radiologists through the early detection and differential diagnosis of neurodegenerative diseases. AIRAscore is also ideal for monitoring the progress of diagnoses made.

Our own experience at Radailogy

We have tested AIRAscore intensively and worked out many technological criteria with the manufacturer for optimal use with Radailogy.

We found a high correlation in the detection of macro- and microangiopathic changes and brain atrophy between our specialists and AIRAscore. In addition, AIRAscore was superior to the human observer in the precision of subtle regional findings in almost all tests. The software provides accurate volumes of all relevant anatomical structures of the cerebrum, cerebellum and brainstem. Early diagnosis was possible, especially for Alzheimer’s patients, and the correlation with clinical and laboratory data was also very high in the follow-up. AIRAscore can also be used for the differential diagnosis of multiple sclerosis and Parkinson’s disease.

The scientific environment

AIRAscore was developed in neuroscientific medical research. From our point of view, the software represents a bridge between innovative university research and direct applicability in everyday clinical practice.

Data to upload to Radailogy

1.5-3.0 Tesla MRI, native 3D T1-weighted gradient-echo sequences, slice thickness 1 mm, echo time ≤ 5 ms, flip angle ≤ 15°

Traumatology plays an important role day and night in medical practices, medical institutes and hospitals. At the same time, it takes up a large proportion of the available working time for medical professionals. On average, only about 10% of all X-rays actually show fractures.

It is with great pleasure that we present Rayvolve, an AI software for fracture detection on X-ray images.

Why Rayvolve matters and how it works

Rayvolve supports rapid and reliable fracture diagnosis on X-ray images of the axial and peripheral skeleton.

Fracture findings are visualized in clear images. The manufacturer speaks of a time saving for radiologists of almost 30% and a significant improvement in diagnostic accuracy with a reduction in false negative results of more than 60%.

Any medical professional can use Rayvolve for each and every trauma patient. In medical institutions, this AI software can also do its work automatically in the background. As a result, fractures are detected immediately after the X-rays are taken, even before the radiologist has seen the studies themselves. These patients can be prioritized, diagnosed and treated accordingly.

Who benefits

Patients, clinicians and radiologists with a more detailed and accurate diagnosis and the reduced likelihood of missed therapy.

Our own experience at Radailogy

Any medical professional can request fracture analysis from this AI software for any individual patient by uploading it to Radailogy quickly and easily. Our telemedicine customers also use Rayvolve as a standard in their daily practice to streamline their workflow.

Our own results at Radailogy with a cohort of a few hundred patients are: sensitivity 93.5%, specificity 87.8%, positive predictive value 92%, negative predictive value 93%.

The scientific evidence

Dupuis M, Delbos L, Veil R, Adamsbaum C. External validation of a commercially available deep learning algorithm for fracture detection in children. Diagn Interv Imaging. 2022 Mar;103(3):151-159.

Date to upload to Radailogy

Digital radiography of a body region in two planes, for example a.p. and lateral or axial

For several years, medicine has been generating significantly more radiation doses than the natural radiation from the cosmos and the earth ever did. The main reason for this is the constantly increasing radiological use of computed tomography (CT). Precisely because CT is and will remain essential for adequate patient care in almost all diagnostic areas, it is up to us to keep the long-documented radiation-induced cancer risk at the lowest possible level.

It is with great pleasure that we present PixelShine, an AI software for CT radiation dose reduction.

Why PixelShine matters

Both in the hospital and in the radiological institute, care is taken to ensure that each patient is only given the necessary radiation dose. However, these low-dose CT protocols almost always produce noisy images, and the CT studies are often difficult to interpret even for medical specialists. In addition, radiologists often have to read CT studies from CT machines from different vendors, which contributes to inconvenience and delays in the workflow.

PixelShine allows two things: Firstly, low-dose CT studies can be carried out for all patients in terms of optimal radiation protection, and PixelShine subsequently generates significantly improved quality from these noisy images, for example in obese patients. Secondly, the lifespan of CT scanners is extended by reducing the load on the CT tubes.

When and how to use PixelShine

PixelShine can be used for studies of any CT device age and vendor. This AI ​​software improves radiological precision by homogenizing the workflow.

PixelShine enables radiologists to read noisy CT studies with a high noise level of image noise in the best possible way, and the radiological quality meets the requirements for diagnostic validity.

Furthermore, hospitals and radiological institutes can carry out low-dose CT studies as standard, integrate PixelShine in post-processing and thus achieve consistently high image quality.

Who benefits

Patients, clinicians, radiologists and the management of hospitals and radiological institutes: care for all patients by minimizing the radiation dose, clear CT images, optimal assessment and discussion of findings, money savings by extending the lifespan of CT scanners.

Our own experience at Radailogy

Our customers send us CT studies to improve image quality with PixelShine and enable optimal diagnostic results. Both in individual cases through the simple upload to Radailogy, as well as as a standard in daily cooperation with our telemedicine.

Selection of scientific publications

Hata A, Yanagawa M, Yoshida Y, et al. Combination of Deep Learning–Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation. American Journal of Roentgenology. 2020;215(6):1321-1328.

Steuwe A, Weber M, Bethge OT, et al. Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography. BJR. 2021;94(1117):20200677.

Brendlin AS, Plajer D, Chaika M, et al. AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging. Diagnostics. 2022;12(1):225.

Hasegawa A, Ishihara T, Thomas MA, Pan T. Noise reduction profile: A new method for evaluation of noise reduction techniques in CT. Medical Physics. 2022;49(1):186-200.

Nagaraj Y, de Jonge G, Andreychenko A, et al. Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting. Eur Radiol. 2022;32(9):6384-6396.

Hasegawa A, Ishihara T, Thomas MA, Pan T. Noise reduction profile: A new method for evaluation of noise reduction techniques in CT. Medical Physics. 2022;49(1):186-200.

Data to upload to Radailogy

CT-Studies of any CT scanner age and vendor

Chest radiography is one of the most common radiological examinations of all, and it is also an essential first-line imaging modality in hospitals and doctors´ offices. The precision in reporting and the communication of results from radiologists to patients and clinicians are the most important success factors for any optimal discussion of findings and therapy.

It is with great pleasure that we present Rayscape, an AI software for chest radiography.

What Rayscape is and how it works

Rayscape increases accuracy in detecting 17 major thoracic pathologies.

Thoracic findings are reported and visualized in tabular form. For each finding, the true positive rate is presented as a degree of probability versus differential diagnoses, especially for pulmonary nodules, pneumonia, and mediastinal pathologies. The clear presentation of findings is a welcome support for knowledge transfer from radiologists to patients and doctors.

Who benefits

Patients, clinicians and radiologists by identifying the most important chest diseases and injuries with a clear presentation of the findings in words and pictures.

Our own experience at Radailogy

We tested Rayscape for several years and brought it to life with the manufacturer. We reviewed the performance data and compared it to our own observations:

Rayscape supports the detection of pulmonary nodules, pulmonary consolidation, pulmonary edema, pulmonary emphysema, interstitial lung disease, tuberculosis, pneumothorax, pleural effusion, atelectasis, cardiomegaly, hilar and mediastinal pathologies, diaphragmatic abnormalities, fractures, scoliosis, catheters and drains. From our point of view, Rayscape achieves the best results in the detection of pneumonia, including viral pneumonia with probability grading from 1 to 6, pulmonary nodules, pneumothorax, pleural effusions, cardiac decompensation, consolidations and atelectasis.

The performance

Rayscape has a high level of accuracy in detecting 17 major chest pathologies. Area Under the Receiver Operating Characteristics (AUROC) is highest for tuberculosis (99.1), pneumothorax (97.4), pulmonary edema (94.7), consolidations (94.6), lowest for interstitial lung disease (81, 5), scoliosis (82), diaphragmatic anomalies (85.4), and hilar and mediastinal pathologies (87.7).

Data to upload to Radailogy

Digital radiography of the chest p.a. or a.p.