BoneXpert validation study in Turkey

Researchers from Koc University Hospital in Istanbul have published a study validating two bone age systems: BoneXpert and Vuno.

292 images were rated with BoneXpert and Vuno Med bone age and compared to a reference formed by two manual ratings.

The accuracy of the two systems was found to be the same, as illustrated in the below Bland Altman plot for girls

Accuracy is one aspect of an automated method – the ability to explain the result is another, and here the two systems are very different, as the authors illustrated by juxtaposing the outputs from BoneXpert and Vuno:

BoneXpert’s main bone age result, 7.54 y, is derived as an average over the 21 tubular bones with equal weights on the bones. In addition, BoneXpert reports a carpal bone age – the average over the 7 carpals.

These results are “explained” by showing the contour of each bone as well as its bone age. Occasionally, a bone is left out due to abnormal shape, but not in this example.

Vuno’s “explanation” is a heat map, depicting where the deep neural net output is most sensitive to changes in the image. In this example, it showed a high intensity in radius, ulna, PP2 and DP3. Since the most reliable bone age is derived by averaging over as many bones as possible, it is of concern that the method collected information from mainly these four bones. BoneXpert’s output suggested that PP2 has bone age 9.0 y, much larger than the average 7.5 y. BoneXpert assigns the same weight to all 21 bones, which tends to average out differences between the bones and this provides precision and standardisation. In a follow-up exam, the Vuno method could – at its own discretion – decide to focus on a different small subset of bones, and this would raise the question whether a change in bone age was due a biological change, or merely a result of emphasising a different subset of bones.

The full article pdf is freely available here

Bone age validation study from Leipzig

Radiologists at the University Hospital in Leipzig have published a comparison of three automated bone age methods: BoneXpert, BoneView form Gleamer and PANDA from Image BiopsyLab

The study collected images of 306 children covering the age range 1–18 years. This wide range allowed the study to reveal how the methods behaved at low and high bone ages.

A reference bone age (denoted  “Ground Truth”) was formed as the average of three human ratings.

The overall agreement between each AI and the reference was almost the same for the three methods, although BoneXpert still showed the best correlation

BoneXpert is the only method that covers the full bone age range 0-19 yr, and it is interesting to see how the other methods handled the low- and high-bone age ends.

BoneView rejected images if the chronological age of the DICOM file was below 3 y and also rejected images with a bone age 17 and above.

PANDA accepted all images, but gave large errors below 5 years as is clearly visible. Also, for females with reference bone age above 15 y, PANDA tended to give predicted bone age not much larger than 15 y – a kind of saturation effect.

The corresponding author is Dr Daniel Gräfe and the full text is available online

AIFI pilot started in five Dutch hospitals

February 17, 2025 – Tiel – The AI For Imaging (AIFI) pilot project is officially launched. The AIFI project focuses on making artificial intelligence (AI) software for medical diagnostics accessible with the aim of reducing the workload for healthcare professionals and specialists via a shared national infrastructure.

The pilot involves a collaboration between five Dutch hospitals, the Dutch Association for Radiology (NVvR), and VZVZ, and is made financially possible by Healthcare Insurers Netherlands.

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Automated bone age at University Hospital Crosshouse

Located in Crosshouse, near Kilmarnock in Scotland, University Hospital Crosshouse is a teaching hospital and the largest facility within NHS Ayrshire & Arran, serving a wide region. In 2023, the hospital adopted BoneXpert, an automated software solution for bone age analysis, to address workflow inefficiencies and improve diagnostic accuracy. As highlighted in the article by the Crosshouse Children’s Fund:

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Google AI talks about BoneXpert AI

We might be crossing a line these days, as AI is not only taking over some of the radiologists’ work, but also taking over talking about it.

Listen to a podcast, which was generated autonomously by Google NotebookLM, from the pdf of the latest paper on the BoneXpert method for autonomous bone age determination.

The podcast is surprisingly good at summarising the general aspects, but for an exact account of the accuracy, please consult the paper.

Auckland Radiology Group become the first private radiology clinic in New Zealand to adopt BoneXpert

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.

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