Auckland Radiology Group – Auckland’s largest independent radiology provider – has become the first private radiology clinic in New Zealand to adopt BoneXpert’s AI software-based medical device for automated bone age and adult height prediction.
Kicky van Leeuwen and the AIR Working Group of Radboud University (Nijmegen, the Netherlands) have performed an external validation of commercial AI systems for bone age and lung nodules.
Visiana welcomes Ruby Hall Clinic as a BoneXpert customer. Ruby Hall Clinic is a flagship hospital in Pune, India, providing healthcare through a system of 3 hospitals and 24 diagnostic centres.
Read more “Ruby Hall Clinic uses BoneXpert software”
In a new, open access paper in European Radiology, Kicky van Leeuwen and a team from Nijmegen surveyed which AIs are actually used in radiology departments in the Netherlands in 2022.
Bone Health Index is significantly lower in children with vertebral fractures than in children without.
BoneXpert has been installed in more than 80% of the major hospitals in the United Kingdom.
Read more “Widespread use of BoneXpert in the United Kingdom”
The accuracy of bone age determination by BoneXpert and Panda in 188 images was reported at ECR.
Federica Zanca from Leuven, together with four co-authors from Switzerland, presented a study comparing two bone age algorithms, BoneXpert from Visiana and Panda (based on deep learning) from ImageBiopsyLab. The study included 188 images taken under real-world conditions across 11 centres in Switzerland. The ground truth was provided by an exceptionally reliable manual rater. BoneXpert’s intended use includes autonomous use, while Panda’s is intended to only assist the radiologist. However, in this study, both algorithms were used without human interference.
The mean absolute deviation between the algorithm and the ground truth was 0.36 y for BoneXpert and 0.42 y for Panda, and this difference was significant with p=0.01.
In the Bland Altman plots below one can clearly see that the agreement is better with BoneXpert.
Figure 1: BoneXpert versus radiologist
Figure 2: Panda versus radiologist
The plots reveal that there are markedly fewer large deviations with BoneXpert.
So what is the clinical signficance of the different performance? The authors adressed this by defining the clinically acceptable limit of agreement to be ±1 year, and they found twice as many such significant deviations for Panda.
The table below summarises all the findings.
The poster is available though myESR (requiring log in)
Table: The deviation between the bone age algorithm and the radiologist
| BoneXpert | Panda | |
| Mean Absolute Deviation | 0.36 y | 0.42 y |
| Root Mean Square Deviation | 0.47 y | 0.55 y |
| Number of deviations > 1 y | 7 | 14 |
| Largest deviation | 1.2 y | 1.9 y |
At ECR, Kicky van Leeuwen of Radboud University Medical Center gave the talk “The rise of artificial intelligence solutions in radiology departments in the Netherlands”.
Van Leeuwen and her co-workers have asked the 69 radiology departments in the Netherlands about their adoption of AI.
They found that the most widely used methods are the ones shown here:
The most popular method is BoneXpert from Danish Visiana, used in 9 departments.
The second use case is detection of lung nodules in CT scans, used by 8 departments – the product Veye Lung Nodules was developed by the Dutch company Aidence (recently acquired by RadNet).
The third-most used method is stroke – left-ventricle occlusion or intracranial haemorrhage detection on CT and CT angiography – probably StrokeViewer from the Dutch vendor Nicolab.
The investigators also inquired about the obstacles for adopting AI and found that the most important were “cost” and “IT”. So, one can infer that the most widely used methods have a favourable balance between cost and benefit, and carry a low IT burden.
Only 3 departments reported use of an “AI platform”.
This talk was also highlighted by AuntMinnie
Note that the survey did not obtain 100% response rate, and indeed Visiana’s records show that there are 14 customers of BoneXpert in the Netherlands, each performing between 100 and 1300 analyses per year.
A new paper documents BoneXpert’s accuracy and self-validation Read more “BoneXpert knows its own limitation”
A questionnaire was sent out to 282 radiologists in Europe using BoneXpert.
The aim was to reveal to what extent radiologists have been replaced by BoneXpert when doing bone age assessment. The result of the survey has now been published as an open access article in Pediatric Radiology. The original questionnaire is in the supplementary information.