Table 4 Overview of integrated AI applications in Southern

3D tumour segmentation of vestibular schwannoma on MRI

Ready for prospective validation in research setting

Transition from 2D measurements to automated 3D volume measurement, resulting in time reduction and quality improvement

Normal/abnormal detection chest X-ray

Fully integrated into clinical workflow (validated on prospective data). AI output directly present in worklist of radiologist. Detailed overlay images presented in separated viewer

 ± 45% of all chest X-ray cases in clinical practice are normal. The algorithm is able to automate reporting for approximately 20% of all normal cases, enhancing clinical efficiency

Lung nodule detection on CT-Thorax

Fully integrated into clinical workflow (validated on prospective data). Output (number of detected lung nodules and percentage of affected lung tissue) presented in PACS worklist and possibility to accept/reject/modify nodule segmentations in PACS viewer

Substantial time reduction in follow-up imaging and improved lung nodule comparisons over time

Bone age measurements on X-ray

Fully integrated into clinical workflow (validated on prospective data). AI report automatically available in PACS

Automated bone age measurements on x-ray, facilitating task differentiation to advanced practitioners

Covid detection and quantification on CT

Fully integrated into clinical workflow (validated on prospective data). AI report automatically available in PACS

Robust quantification of COVID-affected lung parenchyma in all lung segments, significantly enhancing reporting efficiency and quality

Leg angle and distance measurements on X-ray

Fully integrated into clinical workflow (validated on prospective data). AI report is automatically available in PACS. Standardised radiological report based on AI output

Automated leg angle and distance measurements with an AI acceptance rate of approximately 90%

MRI neuro quantification for dementia patients

Fully integrated into clinical workflow (validated on prospective data). AI report automatically available in PACS

Automated quantification of white matter abnormalities and atrophy evaluation

Automatic quality feedback for chest X-ray

Fully integrated in clinical workflow. Automatic quality feedback on iPad after image acquisition

Enhancement of image quality to ensure accurate reporting and prevent the need for patients to return, as low-quality images may otherwise necessitate their return for re-imaging

Large vessel occlusion detection for early stroke detection

Fully integrated into clinical workflow (validated on prospective data)

Fracture detection on X-ray

Implementation phase. Connected to clinical systems and ready for clinical use

Potential impact: decreased reporting time, enhanced diagnostic confidence and subsequently boost job satisfaction, particularly during night and weekend shifts when residents work independently

Scoliosis measurements on X-ray

Implementation phase. Connected to clinical systems and ready for clinical use

Potential impact: automated scoliosis measurements

Automated vertebral fracture assessment on DXA

Development phase, model development and retrospective validation

Potential impact: automated vertebral fracture assessments resulting in significant reduction in reporting time. Prototype has shown positive impact on reader discomfort for annotation

Prostate analysis on MRI

Exploration phase

Potential impact: decreased reporting time by pre-filled structured report based on AI output

AI for tomosynthesis

Exploration phase

Potential impact: decreased reporting time by pre-filled structured report based on AI output

Knee osteoarthritis measurements on X-ray

Exploration phase

Potential impact: automated osteoarthritis measurements. Reduction in reporting time