Conventional radiography (CR) is primarily utilized for fracture diagnosis. Artificial intelligence (AI) for CR is a rapidly growing field aimed at enhancing efficiency and increasing diagnostic accuracy. However, the diagnostic performance of commercially available AI fracture detection solutions (CAAI-FDS) for CR in various anatomical regions, their synergy with human assessment, as well as the influence of industry funding on reported accuracy are unknown. Peer-reviewed diagnostic test accuracy (DTA) studies were identified through a systematic review on Pubmed and Embase. Diagnostic performance measures were extracted especially for different subgroups such as product, type of rater (stand-alone AI, human unaided, human aided), funding, and anatomical region. Pooled measures were obtained with a bivariate random effects model. The impact of rater was evaluated with comparative meta-analysis. Seventeen DTA studies of seven CAAI-FDS analyzing 38,978 x-rays with 8,150 fractures were included. Stand-alone AI studies (n = 15) evaluated five CAAI-FDS; four with good sensitivities (> 90%) and moderate specificities (80–90%) and one with very poor sensitivity (< 60%) and excellent specificity (> 95%). Pooled sensitivities were good to excellent, and specificities were moderate to good in all anatomical regions (n = 7) apart from ribs (n = 4; poor sensitivity / moderate specificity) and spine (n = 4; excellent sensitivity / poor specificity). Funded studies (n = 4) had higher sensitivity (+ 5%) and lower specificity (-4%) than non-funded studies (n = 11). Sensitivity did not differ significantly between stand-alone AI and human AI aided ratings (p = 0.316) but specificity was significantly higher the latter group (p < 0.001). Sensitivity was significant lower in human unaided compared to human AI aided respectively stand-alone AI ratings (both p ≤ 0.001); specificity was higher in human unaided ratings compared to stand-alone AI (p < 0.001) and showed no significant differences AI aided ratings (p = 0.316). The study demonstrates good diagnostic accuracy across most CAAI-FDS and anatomical regions, with the highest performance achieved when used in conjunction with human assessment. Diagnostic accuracy appears lower for spine and rib fractures. The impact of industry funding on reported performance is small.

Ctors for and radiologic types of insufficient bone regenerate during ISKD lengthening? Patients and Methods We analyzed 37 consecutive ISKD femoral lengthening procedures in 35 patients with a mean age 33 ± 11 years and minimum followup of 12 months (average, 27 ± 9 months; range, 12-55 months). The average length gain was 42.8 ± 12.9 mm. Results Eight patients had problems during distraction: seven had “runaway nails” and one had a nondistracting nail. Insufficient bone regenerate developed in eight patients. Important risk factors were a distraction rate greater than 1.5 mm/day (9.1 times higher risk), age 30 years or older, smoking, and lengthening greater than 4 cm. Less important risk factors identified were creation of the osteotomy at the site of previous trauma or surgery and acute correction of associated deformities. We proposed a radiologic classification for failure of bone regeneration: partial regenerate failure (Type I) or complete failure resulting in a segmental defect subdivided according to a length of 3 cm or less (Type IIa) or greater than 3 cm (Type IIb). Conclusions Distraction problems with the ISKD were related mostly to internal malfunction of the lengthening mechanism. A distraction rate greater than 1.5 mm/day should be avoided in femoral intramedullary lengthening. Smoking should be a contraindication for femoral lengthening. Level of Evidence Level II, prognostic study. See Guidelines for Authors for a complete description of levels of evidence....