Radiologic discrepancies in diagnosis of fractures in a Dutch teaching emergency department: a retrospective analysis

Mattijssen-Horstink, Laura; Langeraar, Judith Joëlle; Mauritz, Gert Jan; Stappen, William van der; Baggelaar, Maarten; Tan, Edward

doi:10.1186/s13049-020-00727-8

Cited by 26 publications

(16 citation statements)

References 21 publications

(38 reference statements)

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“…0.13 ± 0.03). Also, the performance of orthopedic attending physicians in this study proved the conclusion of Mattijssen’s research ( Mattijssen-Horstink et al, 2020 ). Missed diagnoses and misdiagnoses really existed in the daily medical work and posed threat to the diagnosis, treatment, and rehabilitation of patients, which still kept demanding for a clinical auxiliary tool, such as AI.…”

Section: Discussionsupporting

confidence: 83%

“…A retrospective analysis in the emergency department illustrated that missed diagnoses and misdiagnoses mostly occurred to the hip fracture (37.3%) rather than other limb fractures, and doctors were more likely to make mistakes between 5 p.m. and 3 a.m. due to fatigue and other factors. After diagnosis correcting, there were more than 55% of patients who still required further treatment such as cast immobilization or even surgery ( Mattijssen-Horstink et al, 2020 ). Hence, there was an urgent demand to find an auxiliary tool to assist clinical fracture diagnosis.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era

Liu

Chen

et al. 2022

Front. Bioeng. Biotechnol.

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Objective: To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of femoral intertrochanteric fracture (FIF), and further compare the performance with human level to confirm the effect and feasibility of the AI algorithm.Methods: 700 X-rays of FIF were collected and labeled by two senior orthopedic physicians to set up the database, 643 for the training database and 57 for the test database. A Faster-RCNN algorithm was applied to be trained and detect the FIF on X-rays. The performance of the AI algorithm such as accuracy, sensitivity, miss diagnosis rate, specificity, misdiagnosis rate, and time consumption was calculated and compared with that of orthopedic attending physicians.Results: Compared with orthopedic attending physicians, the Faster-RCNN algorithm performed better in accuracy (0.88 vs. 0.84 ± 0.04), specificity (0.87 vs. 0.71 ± 0.08), misdiagnosis rate (0.13 vs. 0.29 ± 0.08), and time consumption (5 min vs. 18.20 ± 1.92 min). As for the sensitivity and missed diagnosis rate, there was no statistical difference between the AI and orthopedic attending physicians (0.89 vs. 0.87 ± 0.03 and 0.11 vs. 0.13 ± 0.03).Conclusion: The AI diagnostic algorithm is an available and effective method for the clinical diagnosis of FIF. It could serve as a satisfying clinical assistant for orthopedic physicians.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era

Liu

Chen

et al. 2022

Front. Bioeng. Biotechnol.

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“…This means that frontline physicians can receive real-time recommendations from the algorithm when they are treating multiple trauma patients, as misdiagnoses can occur in a chaotic ER 31 . Regarding pelvic fractures, most structurally and hemodynamically unstable pelvic fractures can be detected by our algorithm, which is especially useful in institutes lacking consulting specialists or experienced medical staff 32 .…”

Section: Discussionmentioning

confidence: 99%

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

Cheng

Wang²,

Chen

et al. 2021

Nat Commun

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Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related radiographic findings on PXRs. Here, we show a universal algorithm can detect most types of trauma-related radiographic findings on PXRs. We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation. PelviXNet yields an area under the receiver operating characteristic curve (AUROC) of 0.973 (95% CI, 0.960–0.983) and an area under the precision-recall curve (AUPRC) of 0.963 (95% CI, 0.948–0.974) in the clinical population test set of 1888 PXRs. The accuracy, sensitivity, and specificity at the cutoff value are 0.924 (95% CI, 0.912–0.936), 0.908 (95% CI, 0.885–0.908), and 0.932 (95% CI, 0.919–0.946), respectively. PelviXNet demonstrates comparable performance with radiologists and orthopedics in detecting pelvic and hip fractures.

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“…We screened for children with old upper limb fractures by ICD code, so we were unable to ascertain the cause of fracture in these children, which is a limitation of our study. Hand phalanx (22.0–26.4%) and elbow (11.4–15.3%) are the most common sites for misdiagnosis and missed diagnosis in children with fractures [ 26 , 41 ], and 80% of these have been found to be because of emergency doctors’ errors in reading radiographs [ 42 ]. In our child population, the distal humerus was the most common fracture site.…”

Section: Discussionmentioning

confidence: 99%

Upper limb pediatric fractures in 22 tertiary children's hospitals, China: a multicenter epidemiological investigation and economic factor analysis of 32,832 hospitalized children

Qiu

Deng²,

Zhao³

et al. 2022

J Orthop Surg Res

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Background Fractures are the most common type of unintentional injury in children, with traumatic upper limb fractures accounting for approximately 80% of all childhood fractures. Many epidemiological investigations of upper limb fractures in children have been conducted, but with the development of society, the patterns of childhood fractures may have changed. This study aimed to analyze the epidemiology and economic cost factors of upper limb fractures in Chinese children. Methods We retrospectively reviewed children with upper limb fractures or old upper limb fractures hospitalized between December 1, 2015, and December 31, 2019, in 22 tertiary children’s hospitals, under China’s Futang Research Center of Pediatric Development. We used the ICD10 codes on the front sheet of their medical records to identify cases and extracted data on age, sex, injury cause, fracture site, treatment, the year of admission and discharge, visiting time, and various costs during hospitalization from the medical record. Results A total of 32,439 children (21,478 boys and 10,961 girls) were identified, of whom 32,080 had fresh fractures and 359 had old fractures. The peak age was 3–6 years in both sexes. A total of 4788 were infants, 14,320 were preschoolers, 10,499 were in of primary school age, and 2832 were adolescent. Fractures were most frequent in autumn (August to October). Admissions peaked at 0 o’clock. Among the 32,080 children with fresh upper limb fractures, the most common fracture site was the distal humerus, with a total of 20,090 fracture events including 13,134 humeral supracondylar fractures and 4914 lateral humeral condyle fractures. The most common cause of injuries was falling over. The most common joint dislocation accompanying upper limb fractures occurred in the elbow, involving 254 cases. Surgery was performed in 31,274 children, and 806 did not receive surgery. Among those with clear operative records, 10,962 children were treated with open reduction and 18,066 with closed reduction. The number of cases was largest in the East China region (Anhui Province, Shandong Province, Jiangsu Province, Zhejiang Province, and Fujian Province), with 12,065 cases overall. Among the 359 children with old fractures, 118 were admitted with a diagnosis of “old humerus fracture,” accounting for the highest proportion; 244 underwent surgical open reduction, 16.16% of whom had osteotomy. For the children with fresh fractures, the average total hospital cost was 10,994 yuan, and the highest average total hospital cost was 14,053 yuan, for humeral shaft fractures. For the children with old fractures, the average total hospital cost was 15,151 yuan, and the highest average total hospital cost was 20,698 yuan, for old ulna fractures. Cost of materials was the principle factor affecting total hospital cost, followed by surgery and anesthesia costs, both in children with fresh fractures and those with old fractures. Significant differences were observed in all hospital costs (P < 0.001) except treatment costs (P = 0.702), between children with fresh fractures and those with old fractures. Among the 32,439 children, full self-payment accounted for the highest proportion of all payment methods, involving 17,088 cases, with an average cost of 11,111 yuan. Conclusion Information on the epidemiological characteristics of childhood fractures suggests that health and safety education and protective measures should be strengthened to prevent upper limb fractures in children. For both fresh and old fractures, the cost of materials was the principal factor affecting total hospital cost, followed by surgery and anesthesia costs. The overall average total hospital cost is higher in children with old fractures than in children with fresh fractures. Among all children, full self-payment, at 53% of children, accounted for the highest proportion of all payment methods. Hospital costs are a headache for those families who will pay on their own. It can lead to a delayed treatment and unhealed fractures or malunion in some children. Therefore, the child trauma care system and training on fractures need to be improved, to reduce the late presentation of fractures. These combined measures will improve children’s quality of life, reduce the expenditure of families, and decrease the public health burden. To provide better medical services for children, authorities must improve the allocation of health resources, establish a comprehensive medical security system for children, and set up more child trauma centers.

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Radiologic discrepancies in diagnosis of fractures in a Dutch teaching emergency department: a retrospective analysis

Cited by 26 publications

References 21 publications

Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era

Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

Upper limb pediatric fractures in 22 tertiary children's hospitals, China: a multicenter epidemiological investigation and economic factor analysis of 32,832 hospitalized children

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