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CXR Suite

AI that adapts to your radiology needs

Powered by ChestLink, the world’s first autonomous AI medical device, ChestEye, and Quality to enhance diagnostic confidence and streamline workflows

Oxipit CXR Suite brings all our expertise under one umbrella, simplifying decision-making and delivering a flexible solution for radiology teams of any size. Whether your focus is automating healthy chest X-ray reporting, enhancing diagnostic accuracy, or streamlining quality assurance, the CXR Suite adapts to your needs.

 

Save time, improve efficiency

  • Automate up to 40% of radiologists’ workflow for healthy chest X-rays using our autonomous reporting tools.
  • Trust 99.9% sensitivity in detecting normal chest X-rays for safe, confident clinical outcomes.

Enhance diagnostic accuracy

  • Experience new level of confidence in reporting while algorithm looks for 75 most common radiological findings.
  • Minimize diagnostic errors with AI-powered real-time quality review tools, acting as an automated safety net.

Future-proof your radiology

  • Scale effortlessly – adaptable for organizations handling from 5,000 to 500,000+ chest X-rays annually.
  • Go live in less than a week with minimal IT setup, ensuring a seamless transition.

Supported findings

In total CXR Suite supports 75 findings
  1. Abnormal Rib
  2. Aortic Sclerosis
  3. Artificial Heart Valve
  4. Azygos Lobe
  5. Barium Swallow
  6. Bowel Gas
  7. Bullous Emphysema
  8. Catheter Malposition
  9. Chest Tube
  10. Congestion
  11. Consolidation
  12. CV Catheter RA Placement
  13. CV Catheter SVC Placement
  14. Cyst
  15. Dislocated Mediastinum
  16. Edema
  17. Elevated Diaphragm
  18. Endovascular Stent
  19. Enlarged Aorta
  20. Enlarged Heart
  21. Esophageal Stent
  22. Fibrosis
  23. Fissural Thickening
  24. Gastric Bubble
  25. Goitre
  26. Granuloma
  27. HD Catheter RA Placement
  28. HD Catheter SVC Placement
  29. Hernia
  30. Hilar Prominence
  31. Hypertension
  32. Hypoventilation
  33. Interstitial Markings
  34. Intra Aortic Balloon
  35. Intubation
  36. Intubation Malposition
  37. Kyphosis
  38. Ligament Ossification
  39. Linear Atelectasis
  40. Lobar Collapse
  41. Loculated Effusion
  42. Lymph Node Calcification
  43. Lymphadenopathy
  44. Mass
  45. Nasogastric Tube
  46. Osteoporosis
  47. Pacemaker
  48. Pericardial Effusion
  49. Pleural Adhesion
  50. Pleural Effusion
  51. Pleural Plaque
  52. Pleural Thickening
  53. Pneumomediastinum
  54. Pneumoperitoneum
  55. Pneumothorax
  56. Pulmonary Cavity
  57. Pulmonary Emphysema
  58. Removed Lung
  59. Respiratory Distress Syndrome
  60. Retrosternal Airspace Obliteration
  61. Rib Resection
  62. Sarcoidosis
  63. Scoliosis
  64. Spinal Compression Fracture
  65. Spinal Degenerative Changes
  66. Spinal Enthesopathy
  67. Spinal Implant
  68. Spondylosis
  69. Sternal Wires
  70. Subcutaneous Emphysema
  71. Thymus
  72. Tracheal Stent
  73. Tuberculosis
  74. Ventricular Assist Device
  75. Widened Mediastinum

CXR Suite in action

Right hydropneumothorax
Right parahilar nodule
Left sided pneumothorax with a small pleural effusion
Bilateral consolidation
Left upper calcified nodule
Right hydropneumothorax
Bilateral consolidation
Right parahilar nodule
Central venous catheter

CXR Suite workflows

This workflow provides radiologists with real-time notifications on 75 key findings, enabling them to review provided insights and submit reports with greater confidence and accuracy.

This workflow enhances radiology efficiency with AI-assisted triage, semi-automated healthy chest X-ray reporting, and a smart analytics dashboard, enabling radiologists to prioritize critical cases, generate reports faster, and maintain full oversight.

This workflow combines AI-assisted triage, semi-automated reporting, and real-time quality monitoring, providing radiologists with automated case prioritization, report generation, and a continuous AI safety net that detects missed abnormalities, ensuring accuracy, efficiency, and high standards of patient care.

This workflow combines AI-assisted triage, semi-automated reporting, real-time quality monitoring, and full autonomy for normal chest X-rays, enabling radiologists to prioritize critical cases, receive continuous diagnostic feedback, and focus entirely on complex cases while optimizing efficiency and resource allocation.

By implementing autonomous AI for normal case detection, we’re addressing a significant workflow challenge while maintaining complete control over quality and safety. For us, the value has been particularly evident in supporting less experienced staff and ensuring quality control. But what’s crucial is that we’re not just looking at efficiency within radiology—we’re considering the entire healthcare pathway. This technology allows us to scale the number of scans we can process with the same number of staff, while maintaining the high-quality standards our patients expect.

 

Willem Grootjans
Assistant Professor and Head of the Imaging Services Group at the department of Radiology, Leiden University Medical Centre

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