Using area and volume measurement via weightbearing CT to detect Lisfranc instability

Bhimani, Rohan; Sornsakrin, Pongpanot; Ashkani-Esfahani, Soheil; Lubberts, Bart; Guss, Daniel; Waryasz, Gregory R.; Kerkhoffs, Gino M. M. J.; DiGiovanni, Christopher W.

doi:10.1002/jor.24970

Cited by 26 publications

(18 citation statements)

References 23 publications

(37 reference statements)

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“…While the Listract test produced a nearly perfect ICC (97%) and it showed to increase in the sensitivity and specificity of both radiographic diastasis and area measurements, the need to reassess the cutoff values for both of these radiographic measurements seems inevitable. The interobserver agreement was moderate and good for diastasis and area measurement, respectively; however, previous reports using 3D weight-bearing CT scans have shown an ICC of >0.96 using 3D volume measurement for this non-invasive imaging method 10,[16][17][18] (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

Section: Discussionmentioning

confidence: 79%

“…Despite various imaging methods, intraoperative examination is the gold standard for confirmation of the diagnosis. 9,10 However, the intraoperative stress examination of the Lisfranc joint by applying a distraction force is not standardized and not a reproducible method. 11 Surgeons apply various amounts of force on the joint to confirm the diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“….Bhimani et al have shown that weight-bearing CT scan can detect Lisfranc instability in C1-M2 joint with a sensitivity and specificity of 0.79 and 96.5 for diastasis and area, respectively 10. They have also shown that 3D volume measurement can have a specificity and sensitivity of 0.98 and 0.92, respectively.…”

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confidence: 99%

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Listract test: a Standardized Assessment Method for Isolated Lisfranc Instability in Cadaver Models

Mathur

Osei-Hwedieh

Sayyed-Hosseinian

et al. 2023

Preprint

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Background: The gold standard for diagnosis of Lisfranc instability is direct visualization in the operation room while the examination techniques is still unstandardized and non-reproducible. We aimed to introduce a novel reproducible intraoperative mechanical testing method (Listract test) for intraoperative isolated Lisfranc instability assessment. Methods: The Lisfranc ligament between the first cuneiform (C1) and second metatarsus (M2) in eight lower leg cadaveric specimens were dissected to replicate C1-M2 Lisfranc instability by eight foot and ankle surgeons. The 50N distraction force was applied in the direction of the C1-M2 ligament. Three methods of fixation - flexible fixation, metal screw, and bio-absorbable screw- were used to fix the injury, and Listract test was applied again after fixation. Besides intraoperative assessment, C1-M2 diastasis and area were measured using radiographs for assessment of Lisfranc instability. Results: The sensitivity and specificity of the Listract test for detection of C1-M2 instability were 100% and 100% intraoperatively, 33.3% and 95.2% using radiographic diastasis measurement, and 63.2% and 38% using area measurement, respectively. The Listract test had a specificity and sensitivity of 100% and 96% for intraoperative assessment, 87.5% and 64.3 for radiographic C1-M2 diastasis, and 48% and 50% for radiographic area. Conclusion: The Listract test is a simple, reproducible, and replicable intraoperative method for evaluating the Lisfranc joint for instability. Developing a device with this mechanism can help clinicians confirm the diagnosis and provide appropriate treatment particularly for equivocal diagnoses.

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Section: Discussionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

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Listract test: a Standardized Assessment Method for Isolated Lisfranc Instability in Cadaver Models

Mathur

Osei-Hwedieh

Sayyed-Hosseinian

et al. 2023

Preprint

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“…in the case of a far distal fracture. A combination with area and volume measurements could possibly reduce a larger error [ 27 ] and could be investigated in a further study. Furthermore, information about the medical history of the cadavers was limited.…”

Section: Discussionmentioning

confidence: 99%

Restoration of the patient-specific anatomy of the distal fibula based on a novel three-dimensional contralateral registration method

et al. 2022

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Purpose Posttraumatic fibular malunion alters ankle joint biomechanics and may lead to pain, stiffness, and premature osteoarthritis. The accurate restoration is key for success of reconstructive surgeries. The aim of this study was to analyze the accuracy of a novel three-dimensional (3D) registration algorithm using different segments of the contralateral anatomy to restore the distal fibula. Methods Triangular 3D surface models were reconstructed from computed tomographic data of 96 paired lower legs. Four segments were defined: 25% tibia, 50% tibia, 75% fibula, and 75% fibula and tibia. A surface registration algorithm was used to superimpose the mirrored contralateral model on the original model. The accuracy of distal fibula restoration was measured. Results The median rotation error, 3D distance (Euclidean distance), and 3D angle (Euler’s angle) using the distal 25% tibia segment for the registration were 0.8° (− 1.7–4.8), 2.1 mm (1.4–2.9), and 2.9° (1.9–5.4), respectively. The restoration showed the highest errors using the 75% fibula segment (rotation error 3.2° (0.1–8.3); Euclidean distance 4.2 mm (3.1–5.8); Euler’s angle 5.8° (3.4–9.2)). The translation error did not differ significantly between segments. Conclusion 3D registration of the contralateral tibia and fibula reliably approximated the premorbid anatomy of the distal fibula. Registration of the 25% distal tibia, including distinct anatomical landmarks of the fibular notch and malleolar colliculi, restored the anatomy with increasing accuracy, minimizing both rotational and translational errors. This new method of evaluating malreductions could reduce morbidity in patients with ankle fractures. Level of evidence IV

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“…9 Weightbearing CT (WBCT) affords a 3-dimensional (3D) view of the midfoot under physiologic load while also affording a contralateral comparison. 1,23 In a recent study, although suffering from a small population, WBCT modality showed a sensitivity of up to 92.3% and specificity of up to 97.7% in the detection of subtle Lisfranc instability. 1 Nonetheless, WBCT remains unavailable at many clinical sites.…”

Section: Introductionmentioning

confidence: 96%

Deep Learning Algorithms Improve the Detection of Subtle Lisfranc Malalignments on Weightbearing Radiographs

et al. 2022

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Background: Detection of Lisfranc malalignment leading to the instability of the joint, particularly in subtle cases, has been a concern for foot and ankle care providers. X-ray radiographs are the mainstay in the diagnosis of these injuries; thus, improving the performance of clinicians in interpreting radiographs can noticeably affect the quality of health care in these patients. Here we assessed the performance of deep learning algorithms on weightbearing radiographs for detection of Lisfranc joint malalignment in patients with Lisfranc instability. Methods: In a retrospective study, 640 patients with Lisfranc malalignment leading to instability were recruited plus 640 individuals with uninjured feet and healthy Lisfranc joint as the control group. All radiographs were screened by orthopaedic surgeons. Two deep learning models were trained, validated, and tested (in a ratio 80:10:10) using a single-view (anteroposterior) and 3-view (anteroposterior, lateral, oblique) radiographs. The performances of the models were reported as sensitivity, specificity, positive and negative predictive values, accuracy, F score, and area under the curve (AUC). Results: No significant differences were observed between the patients and the controls regarding age, gender, race, and body mass index. The best deep learning algorithm outperformed our human interpreters (<1% vs ~10% misdiagnosis), 94.8% sensitivity, 96.9% specificity, 98.6% accuracy, 95.8% F score, and 99.4% AUC. Conclusion: Deep learning methods have shown promising potential in acting as an assistant interpreter of radiographic images in patients with Lisfranc malalignment. Developing these algorithms can hasten and improve the accuracy of diagnosis and reduce further costs and burdens on the patients and health care system. Level of Evidence: Level III, case-control Machine Learning study.

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Using area and volume measurement via weightbearing CT to detect Lisfranc instability

Cited by 26 publications

References 23 publications

Listract test: a Standardized Assessment Method for Isolated Lisfranc Instability in Cadaver Models

Listract test: a Standardized Assessment Method for Isolated Lisfranc Instability in Cadaver Models

Restoration of the patient-specific anatomy of the distal fibula based on a novel three-dimensional contralateral registration method

Deep Learning Algorithms Improve the Detection of Subtle Lisfranc Malalignments on Weightbearing Radiographs

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