Artificial intelligence software BoneView helps radiologists and emergency physicians detect and localise fractures


BoneViewBoneView, an artificial intelligence (AI) software from French company GLEAMER, provides a gain of 8.7% increase in sensitivity and a 4.1% gain in specificity without loss of reading speed, and thus is an effective aid for radiologists and emergency physicians in the detection and localisation of appendicular skeletal fractures.

The interpretation of radiographs suffers from an ever-increasing workload in emergency and radiology departments, the study investigators write in Radiology, while missed fractures represent up to 80% of diagnostic errors in the emergency department. Lead author Loïc Duron (Adolphe de Rothschild Foundation Hospital, Paris, France) and colleagues therefore set out to assess the performance of BoneView in an effort to improve radiograph interpretation.

“To our knowledge, this is the first study that evaluates the performance of AI‐assisted reading for radiographic fracture detection over the entire appendicular skeleton and on multicentre data, from a variety of imaging systems, some of which were not present in the training database,” comments Duron, who is currently doing a science thesis on AI in medical imaging.

“This study is the result of significant collaborative work between specialists in radiology, data science, biostatistics, and GLEAMER teams to evaluate the clinical performance of BoneView. This is an important step that quantifies and scientifically validates the benefits of this AI solution available to healthcare institutions, in a context where several solutions are marketed without published objective results,” adds Antoine Feydy, osteoarticular radiologist at Cochin Hospital (Paris, France). “Publication in Radiology, a journal with the highest impact factor in the field, reflects on the one hand the rigor of the methodology used but also the magnitude of the obtained results. ”

“We are delighted and proud of the publication in Radiology of our ambitious clinical study with BoneView, whose results are unequivocal on the benefits of the cross‐interpretation of AI and the physician: 30% decrease in the rate of undetected fractures, while reducing the radiographs reading time by 15%, on exams specifically selected for their difficulty, i.e. with so‐called non‐obvious fractures. In 10 years, the number of exams to be analysed by radiologists has doubled, while the number of radiologists has increased by only 20%. We are continuing our developments to improve and enrich our range of AI software to progressively automate the diagnosis of standard radiography to ensure very high reliability of the examination and optimal patient care,” says Christian Allouche, CEO and co‐founder of GLEAMER.

Prior to the study, the BoneView AI system was trained on 60,170 radiographs obtained from trauma patients. For the study, between 2016 and 2018, 600 adult patients in whom radiographs were obtained after a recent trauma, with or without one or more fractures of the shoulder, arm, hand, pelvis, leg, or foot, at 17 French imaging centres, were retrospectively included. Six radiologists and six emergency physicians were asked to detect and localise fractures with and without the help of BoneView software. Sensitivity, specificity, and reading times with and without assistance were compared after averaging the performance of each reader. AI assistance improved physician sensitivity per patient by 12% (and 22% for patients with multiple fractures), and specificity by 5%; it also reduced the average number of false positives per patient by 42% in patients without fractures and the average reading time by 15%. Finally, the stand‐alone performance of a newer version of the AI system was superior to that of all unassisted readers, including osteoarticular expert radiologists.

GLEMER state: “The help of AI improves the diagnostic performance of radiologists and emergency physicians, which will allow better patient management from their first imaging examination. Among the expected consequences, the help of AI should allow to improve the specificity of the complementary exams prescribed after the radiography, to avoid delays in care, and to direct patients into the right therapeutic pathway.”


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