Critical Portions of a Foot or Ankle Surgical Procedure From Patient and Surgeon Perspectives.

BACKGROUND: Over the past decade, overlapping procedures in orthopaedic surgery have come under increased public scrutiny. Central to this discussion is what should constitute a "critical portion" of any surgical procedure-a definition which may differ between patients and surgeons. This study therefore aimed to assess which components of three common foot and ankle procedures are considered "critical" from both the patient and surgeon perspectives. METHODS: For this survey-based study, questionnaires were administered to patients who presented to an orthopaedic foot and ankle clinic and separately administered to foot and ankle surgeons through e-mail. The questionnaires broached all steps involved in three common foot and ankle procedures: open reduction and internal fixation of ankle fracture, Achilles tendon repair, and ankle arthroscopy. Respondents were asked to characterize each step as "always critical," "often critical," sometimes critical," rarely critical," or "never critical." A combined "always critical" and "often critical" response rate of greater than 50% was used to define a step as genuinely critical. Patient and surgeon responses were thereafter compared using Mann-Whitney U and Kruskal-Wallis tests (P-value <0.05 was considered significant). RESULTS: Notably, both patients and surgeons considered informed consent, preoperative marking of the surgical site, preoperative time-out, surgical soft-tissue dissection, and certain procedure-specific steps (critical portions) of these procedures. By contrast, only patients considered skin incision and wound closure to be critical steps. CONCLUSION: Patients and surgeons were largely in agreement as to what should comprise the critical portions of several common foot and ankle procedures. Certain discrepancies did exist, however, such as skin incision and closure, and both groups were also in general agreement regarding what was not considered a critical component of these operations. Such findings highlight a potential opportunity for improved preoperative patient education and patient-physician communication. LEVEL OF EVIDENCE: Level IV: Evidence from well-designed case-control or cohort studies.

Unveiling Syndesmotic Malreduction: A Proof-of-Concept towards Portable Ultrasound Detection.

Objectives: To evaluate the utility and diagnostic performance of portable handheld ultrasound for evaluating fibular rotation at the distal tibiofibular articulation after syndesmotic disruption. Methods: Four above-the-knee cadaveric specimens were included. Syndesmotic disruption was precipitated by transecting the Anterior Inferior Tibiofibular Ligament, Interosseous Ligament, and Posterior Inferior Tibiofibular Ligament. Thereafter, a proximal fibular osteotomy was performed, and three conditions were modeled at the distal syndesmosis: 1) reduced, 2) 5 degree internal rotation malreduction, and 3) 5 degree external rotation malreduction. Two blinded observers performed separate ultrasonographic examinations for each condition at the level of both the anterior and posterior distal tibiofibular articular surfaces. Syndesmotic gap penetrance, defined as the ability of the P-US to generate signal between the distal fibula and tibia at the level of the incisura, was graded positive if the sonographic waves penetrated between the distal tibiofibular joint and negative if no penetrating waves were detected. The accuracy measures of the anterior and posterior gap penetrance were evaluated individually. Results: Our preliminary results showed that posterior gap penetrance showed good performance when detecting either internal or external rotational malreduction of the fibula with very good specificity (87.5%) and PPV (90.0%). On the other hand, the anterior gap penetrance showed limited performance when detecting either form of rotational malreduction. Conclusion: We introduced a novel sign, the "gap penetrance sign", best measured from the posterior ankle, which can accurately detect syndesmotic malreduction using P-US in a manner that does not require specific quantitative measurements and is readily accessible to early P-US users.

Case Report: Portable handheld ultrasound facilitates intra-articular injections in articular foot pathologies.

Background: Intra-articular injections are commonly used to manage joint pathologies, including osteoarthritis. While conventional ultrasound (US) guidance has generally improved intra-articular injection accuracy, forefoot and midfoot joint interventions are still often performed without imaging guidance. This pilot study aims to evaluate the efficacy of office-based, portable ultrasound (P-US) guided intra-articular injections for forefoot and midfoot joint pain caused by various degenerative pathologies. Methods: A retrospective analysis was conducted on a series of consecutive patients who underwent P-US guided intra-articular injections following a chief complaint of forefoot or midfoot joint pain. Patients reported their pain levels using the Visual Analog Scale (VAS) pre-injection and at 3 months follow-up. The procedure was performed by an experienced foot and ankle surgeon using a linear array transducer for guidance, and a 25-gauge needle was used to inject a combination of 2 cc 1% lidocaine and 12 cc of Kenalog (40 mg/ml). Complications and pain scores were analyzed using a paired t-test and p < 0.05 was considered significant. Results: We included 16 patients, 31% male and 69% female with a mean age (±SD) of 61.31 (±12.04) years. None of the patients experienced immediate complications following the intervention. The mean pre-injection VAS score was significantly reduced from 5.21 (±2.04) to a mean of 0.50 (±1.32) at 3 months follow-up (P < 0.001). Thirteen patients reported complete resolution of pain at the 3-month follow-up. No adverse events were reported throughout the duration of the study. Conclusion: This pilot study suggests P-US-guided intra-articular injections offer a safe and effective method for managing forefoot and midfoot joint pain caused by various arthritic pathologies. Further research is warranted to establish the long-term efficacy and comparative effectiveness of P-US-guided injections in larger patient cohorts as compared to non-image guided injections.

Using machine learning in the prediction of symptomatic venous thromboembolism following ankle fracture.

BACKGROUND: Venous thromboembolism (VTE) is a major cause of morbidity and mortality in the trauma setting, and both prediction and prevention of VTE have long been a concern for healthcare providers in orthopedic surgery. The purpose of this study was to evaluate the use of novel statistical analysis and machine-learning in predicting the risk of VTE and the usefulness of prophylaxis following ankle fractures. METHODS: The medical profiles of 16,421 patients with ankle fractures were screened retrospectively for symptomatic VTE. In total, 238 patients sustaining either surgical or nonsurgical treatment for ankle fracture with subsequently confirmed VTE within 180 days following the injury were placed in the case group. Alternatively, 937 patients who sustained ankle fractures managed similarly but had no documented evidence of VTE were randomly chosen as the control group. Individuals from both the case and control populations were also divided into those who had received VTE prophylaxis and those who had not. Over 110 variables were included. Conventional statistics and machine learning methods were used for data analysis. RESULTS: Patients who had a motor vehicle accident, surgical treatment, increased hospital stay, and were on warfarin were shown to have a higher incidence of VTE, whereas patients who were on statins had a lower incidence of VTE. The highest Area Under the Receiver Operating Characteristic Curves (AUROC) showing the performance of our machine learning approach was 0.88 with 0.94 sensitivity and 0.36 specificity. The most balanced performance was seen in a model that was trained using selected variables with 0.86 AUROC, 0.75 sensitivity, and 0.85 specificity. CONCLUSION: By using machine learning, this study successfully pinpointed several predictive factors linked to the occurrence or absence of VTE in patients who experienced an ankle fracture. Training these algorithms using larger, more granular, and multicentric data will further increase their validity and reliability and should be considered the standard for the development of such algorithms. LEVEL OF EVIDENCE: Case-Control study - 3.

Systematic review of machine-learning models in orthopaedic trauma.

Aims: Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods: A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results: A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion: The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.

Correlation Between Statin Use and Symptomatic Venous Thromboembolism Incidence in Patients With Ankle Fracture: A Machine Learning Approach.

BACKGROUND: Identifying factors that correlate with the incidence of venous thromboembolism (VTE) has the potential to improve VTE prevention and positively influence decision-making regarding prophylaxis. In this study, we aimed to investigate the correlation between statin consumption and the incidence of VTE in patients who sustained an ankle fracture. METHODS: In this retrospective, case-controlled study, cases were those who developed VTE and controls were those who had no VTE, and the ratio was 1:4. Patients' demographics, history of hyperlipidemia, and reported statins use were obtained. A random forest classifier (RFC) model was used to predict whether statin consumers were at risk of VTE after ankle fracture regardless of VTE prophylaxis administration based on statin consumption, body mass index (BMI), age, and biological sex. RESULTS: Of the 1175 patients with ankle fractures, 238 had confirmed VTE (case group), and 937 had no symptomatic VTE (control group; ratio 1:4). Fifty (21%) cases and 407 (43%) controls were on a statin. Statin users had a significantly lower incidence of VTE after ankle fracture, odds ratio (OR) = 0.35, 95% CI: 0.25, 0.49, P < .001. Our model showed an area under the receiving operator curve (AUROC) of 78%, a sensitivity of 73%, and a specificity of 83% in predicting the risk of VTE. The importance of the predictors of VTE, other than the use of statins (model importance = 0.1), were age (model importance of 0.72), BMI (model importance of 0.24), and biological sex (model importance of 0.02). CONCLUSION: Statins were significantly associated with a lower rate of VTE in our population of patients who sustained an ankle fracture. LEVELS OF EVIDENCE: 3.

The use of deep learning enables high diagnostic accuracy in detecting syndesmotic instability on weight-bearing CT scanning.

PURPOSE: Delayed diagnosis of syndesmosis instability can lead to significant morbidity and accelerated arthritic change in the ankle joint. Weight-bearing computed tomography (WBCT) has shown promising potential for early and reliable detection of isolated syndesmotic instability using 3D volumetric measurements. While these measurements have been reported to be highly accurate, they are also experience-dependent, time-consuming, and need a particular 3D measurement software tool that leads the clinicians to still show more interest in the conventional diagnostic methods for syndesmotic instability. The purpose of this study was to increase accuracy, accelerate analysis time, and reduce interobserver bias by automating 3D volume assessment of syndesmosis anatomy using WBCT scans. METHODS: A retrospective study was conducted using previously collected WBCT scans of patients with unilateral syndesmotic instability. One-hundred and forty-four bilateral ankle WBCT scans were evaluated (48 unstable, 96 control). We developed three deep learning models for analyzing WBCT scans to recognize syndesmosis instability. These three models included two state-of-the-art models (Model 1-3D Convolutional Neural Network [CNN], and Model 2-CNN with long short-term memory [LSTM]), and a new model (Model 3-differential CNN LSTM) that we introduced in this study. RESULTS: Model 1 failed to analyze the WBCT scans (F1 score = 0). Model 2 only misclassified two cases (F1 score = 0.80). Model 3 outperformed Model 2 and achieved a nearly perfect performance, misclassifying only one case (F1 score = 0.91) in the control group as unstable while being faster than Model 2. CONCLUSIONS: In this study, a deep learning model for 3D WBCT syndesmosis assessment was developed that achieved very high accuracy and accelerated analytics. This deep learning model shows promise for use by clinicians to improve diagnostic accuracy, reduce measurement bias, and save both time and expenditure for the healthcare system. LEVEL OF EVIDENCE: II.

Implementing automated 3D measurements to quantify reference values and side-to-side differences in the ankle syndesmosis.

Detection of syndesmotic ankle instability remains challenging in clinical practice due to the limitations of two-dimensional (2D) measurements. The transition to automated three-dimensional (3D) measurement techniques is on the verge of a breakthrough but normative and side-to-side comparative data are missing. Therefore, our study aim was two-fold: (1) to establish 3D anatomical reference values of the ankle syndesmosis based on automated measurements and (2) to determine to what extent the ankle syndesmosis is symmetric across all 3D measurements. Patients without syndesmotic pathology with a non-weight-bearing CT scan (NWBCT; N = 38; Age = 51.6 ± 17.43 years) and weight-bearing CT scan (WBCT; N = 43; Age = 48.9 ± 14.3 years) were retrospectively included. After training and validation of a neural network to automate the segmentation of 3D ankle models, an iterative closest point registration was performed to superimpose the left on the right ankle. Subsequently, 3D measurements were manually and automatically computed using a custom-made algorithm and side-to-side comparison of these landmarks allowed one to investigate symmetry. Intra-observer analysis showed excellent agreements for all manual measurements (ICC range 0.85-0.99) and good (i.e. < 2.7° for the angles and < 0.5 mm for the distances) accuracy was found between the automated and manual measurements. A mean Dice coefficient of 0.99 was found for the automated segmentation framework. The established mean, standard deviation and range were provided for each 3D measurement. From these data, reference values were derived to differ physiological from pathological syndesmotic alignment. Furthermore, side-to-side symmetry was revealed when comparing left to right measurements (P > 0.05). In clinical practice, our novel algorithm could surmount the current limitations of manual 2D measurements and distinguish patients with a syndesmotic ankle lesion from normal variance.

Republication of "Stress Fractures of the Foot and Ankle in Athletes".

Stress fractures of the foot and ankle are common injuries in athletes. Management differs considerably based on fracture location and predisposing factors. Repetitive loading of the foot and ankle in athletes should result in physiologic bone remodeling in accordance with Wolff's law. However, when there is not sufficient time for complete healing to occur before additional loads are incurred, this process can instead lead to stress fracture. Assessment of the athlete's training regimen and overall bone health is paramount to both the discovery and treatment of these injuries, although diagnosis is often delayed in the setting of normal-appearing initial radiographs. While most stress fractures of the foot or ankle can usually be treated nonoperatively with a period of activity modification, fractures in certain locations are considered "high risk" due to poor intrinsic healing and may warrant more proactive operative management.

Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN).

PURPOSE: Convolutional neural networks (CNNs) are increasingly being developed for automated fracture detection in orthopaedic trauma surgery. Studies to date, however, are limited to providing classification based on the entire image-and only produce heatmaps for approximate fracture localization instead of delineating exact fracture morphology. Therefore, we aimed to answer (1) what is the performance of a CNN that detects, classifies, localizes, and segments an ankle fracture, and (2) would this be externally valid? METHODS: The training set included 326 isolated fibula fractures and 423 non-fracture radiographs. The Detectron2 implementation of the Mask R-CNN was trained with labelled and annotated radiographs. The internal validation (or 'test set') and external validation sets consisted of 300 and 334 radiographs, respectively. Consensus agreement between three experienced fellowship-trained trauma surgeons was defined as the ground truth label. Diagnostic accuracy and area under the receiver operator characteristic curve (AUC) were used to assess classification performance. The Intersection over Union (IoU) was used to quantify accuracy of the segmentation predictions by the CNN, where a value of 0.5 is generally considered an adequate segmentation. RESULTS: The final CNN was able to classify fibula fractures according to four classes (Danis-Weber A, B, C and No Fracture) with AUC values ranging from 0.93 to 0.99. Diagnostic accuracy was 89% on the test set with average sensitivity of 89% and specificity of 96%. External validity was 89-90% accurate on a set of radiographs from a different hospital. Accuracies/AUCs observed were 100/0.99 for the 'No Fracture' class, 92/0.99 for 'Weber B', 88/0.93 for 'Weber C', and 76/0.97 for 'Weber A'. For the fracture bounding box prediction by the CNN, a mean IoU of 0.65 (SD ± 0.16) was observed. The fracture segmentation predictions by the CNN resulted in a mean IoU of 0.47 (SD ± 0.17). CONCLUSIONS: This study presents a look into the 'black box' of CNNs and represents the first automated delineation (segmentation) of fracture lines on (ankle) radiographs. The AUC values presented in this paper indicate good discriminatory capability of the CNN and substantiate further study of CNNs in detecting and classifying ankle fractures. LEVEL OF EVIDENCE: II, Diagnostic imaging study.

Arthroscopic assessment of syndesmotic instability: Are we pulling correctly in the coronal plane?

BACKGROUND: While the lateral hook test (LHT) has been widely used to arthroscopically evaluate syndesmotic instability in the coronal plane, it is unclear whether the angulation of the applied force has any impact on the degree of instability. We aimed to determine if changing the direction of the force applied while performing the LHT impacts the amount of coronal diastasis observed in subtle syndesmotic injuries. METHODS: In 10 cadaveric specimens, arthroscopic evaluation of the syndesmotic joint was performed by measuring anterior and posterior-third coronal plane diastasis in the intact state, and repeated after sequential transection of the 1) anterior inferior tibiofibular ligament (AITFL), 2) interosseous ligament (IOL), and 3) posterior inferior tibiofibular ligament (PITFL). In all scenarios, LHT was performed under 100 N of laterally directed force. Additionally, LHT was also performed under: 1) anterior inclination of 15 degrees and 2) posterior inclination of 15 degrees in intact and AITFL+IOL deficient state. RESULTS: Compared to the intact state, the syndesmosis became unstable after AITFL +IOL transection under laterally directed force with no angulation (p = 0.029 and 0.025 for anterior and posterior-third diastasis, respectively), which worsened with subsequent PITFL transection (p = <0.001). Moreover, there was no statistical difference in anterior and posterior-third coronal diastasis in both intact and AITFL+IOL deficient states under neutral, anterior, and posteriorly directed force (p-values ranging from 0.816 to 0.993 and 0.396-0.80, respectively). However, in AITFL+IOL transected state, posteriorly directed forces resulted in greater diastasis than neutral or anteriorly directed forces. CONCLUSIONS: Angulation of the applied force ranging from 15 degrees anteriorly to 15 degrees posteriorly during intraoperative LHT has no effect on coronal plane measurements in patients with subtle syndesmotic instability. On the other hand, posteriorly directed forces result in more sizable diastasis, potentially increasing their sensitivity. CLINICAL RELEVANCE: When arthroscopically evaluating subtle syndesmotic instability, clinicians should assess coronal diastasis with the hook angled 15 degrees posteriorly.

Short-Term Risk Factors for Subtalar Arthrodesis After Primary Tibiotalar Arthrodesis.

While adjacent joint arthritis is a recognized long-term downside of primary tibiotalar arthrodesis (TTA), few studies have identified risk factors for early subtalar arthrodesis (STA) after TTA. This study aims to identify the risk factors for STA within the first few years following TTA. All patients older than 18 years undergoing TTA between 2008 and 2016 were identified retrospectively. Demographic data and comorbidities were collected alongside prior operative procedures, postoperative complications, and subsequent STA. Pre-and postoperative Kellgren-Lawrence osteoarthritis grade of the subtalar joint and postoperative radiographic alignment were obtained. A total of 240 patients who underwent primary TTA were included in this study with median follow up of 13.8 months. Twenty patients (8.3%) underwent STA after TTA due to symptomatic nonunion of TTA in 13 (65%), progression of symptomatic subtalar osteoarthritis (OA) in 4 (20%), and symptomatic nonunion of primary TTA combined progressively symptomatic subtalar OA in 2 (10%). Preoperative radiographic subtalar OA severity and postoperative radiographic alignment were not correlated with subsequent STA. Diabetes mellitus, Charcot arthropathy, neuropathy, alcohol use, substance use disorder, and psychiatric disease were significantly associated with having a subsequent STA. The most common postoperative contributing factor for subsequent STA following primary TTA was the salvage of symptomatic ankle nonunion rather than subtalar joint disease. Patients considering an ankle fusion should be counseled of the risk of subsequent STA, especially if they have risk factors that include diabetes, Charcot arthropathy, neuropathy, alcohol use, substance use disorder, or psychiatric disease.

Fluoroscopic Evaluation of the Role of Syndesmotic Injury in Lateral Ankle Instability in a Cadaver Model.

BACKGROUND: There is a high prevalence of concomitant lateral ankle ligament injuries and syndesmotic ligamentous injuries. However, it is unclear whether syndesmotic ligaments directly contribute toward the stability of the lateral ankle. Therefore, the aim of this study was to fluoroscopically evaluate the role of the syndesmotic ligaments in stabilizing the lateral ankle. METHODS: Twenty-four cadaveric specimens were divided into 3 groups and fluoroscopically evaluated for lateral ankle stability with all syndesmotic and ankle ligaments intact and then following serial differential ligamentous transection. Group 1: (1) anterior talofibular ligament (ATFL), (2) calcaneofibular ligament (CFL), and (3) posterior talofibular ligament (PTFL). Group 2: (1) anterior inferior tibiofibular ligament (AITFL), (2) interosseous ligament (IOL), (3) posterior inferior tibiofibular ligament (PITFL), (4) ATFL, (5) CFL, and (6) PTFL. Group 3: (1) AITFL, (2) ATFL, (3) CFL, (4) IOL, (5) PTFL, and (6) PITFL. At each transection state, 3 loading conditions were used: (1) anterior drawer test performed using 50 and 80 N of direct force, (2) talar tilt <1.7 Nm torque, and (2) lateral clear space (LCS) <1.7 Nm torque. These measurements were in turn compared with those of the stressed intact ligamentous state. Wilcoxon rank-sum test was used to compare the findings of each ligamentous transection state to the intact state. A P value <.05 was considered statistically significant. RESULTS: The lateral ankle remained stable after transection of all syndesmotic ligaments (AITFL, IOL, PITFL). However, after additional transection of the ATFL, the lateral ankle became unstable in varus and anterior drawer testing conditions (P values ranging from .036 to .012). Lateral ankle instability was also observed after transection of the ATFL and AITFL in varus and anterior drawer testing conditions (P values ranging from .036 to .012). Subsequent transection of the CFL and PTFL worsened the lateral ankle instability. CONCLUSION: Our findings suggest that isolated syndesmosis disruption does not result in lateral ankle instability. However, the lateral ankle became unstable when the syndesmosis was injured along with ATFL disruption. CLINICAL RELEVANCE: When combined with ATFL release, disruption of the syndesmosis appeared to destabilize the lateral ankle.

Detection of ankle fractures using deep learning algorithms.

BACKGROUND: Early and accurate detection of ankle fractures are crucial for optimizing treatment and thus reducing future complications. Radiographs are the most abundant imaging techniques for assessing fractures. Deep learning (DL) methods, through adequately trained deep convolutional neural networks (DCNNs), have been previously shown to faster and accurately analyze radiographic images without human intervention. Herein, we aimed to assess the performance of two different DCNNs in detecting ankle fractures using radiographs compared to the ground truth. METHODS: In this retrospective case-control study, our DCNNs were trained using radiographs obtained from 1050 patients with ankle fracture and the same number of individuals with otherwise healthy ankles. Inception V3 and Renet-50 pretrained models were used in our algorithms. Danis-Weber classification method was used. Out of 1050, 72 individuals were labeled as occult fractures as they were not detected in the primary radiographic assessment. Single-view (anteroposterior) radiographs was compared with 3-views (anteroposterior, mortise, lateral) for training the DCNNs. RESULTS: Our DCNNs showed a better performance using 3-views images versus single-view based on greater values for accuracy, F-score, and area under the curve (AUC). The highest sensitivity was 98.7 % and specificity was 98.6 % in detection of ankle fractures using 3-views using inception V3. This model missed only one fracture on radiographs. CONCLUSION: The performance of our DCNNs showed that it can be used for developing the currently used image interpretation programs or as a separate assistant solution for the clinicians to detect ankle fractures faster and more precisely. LEVEL OF EVIDENCE: III.

Isolated injuries to the lateral ankle ligaments have no direct effect on syndesmotic stability.

PURPOSE: This study aim was to detect the impact of lateral ankle ligaments injury on syndesmotic laxity when evaluated arthroscopically in a cadaveric model. The null hypothesis was that lateral ankle ligament injury does not affect the stability of syndesmosis. METHODS: Sixteen fresh-frozen above-knee amputated cadaveric specimens were divided into two groups of eight specimens that underwent arthroscopic evaluation of the distal tibiofibular joint. In both the groups, the assessment was first done with all syndesmotic and ankle ligaments intact. Thereafter, Group 1 underwent sequential transection of the three lateral ankle ligaments first to identify the effects of lateral ligament injury: (1) anterior talofibular ligament (ATFL), (2) calcaneofibular ligament (CFL), (3) posterior talofibular ligament (PTFL), then followed by the syndesmotic ligaments, (4) AITFL, (5) Interosseous ligament (IOL), and (6) PITFL. Group 2 underwent sequential transection of the (1) AITFL, (2) ATFL, (3) CFL, (4) IOL, (5) PTFL, and (6) PITFL, which represent the most commonly injured pattern in ankle sprain. In all scenarios, four loading conditions were considered under 100 N of direct force: (1) unstressed, (2) a lateral fibular hook test, (3) anterior to posterior (AP) fibular translation test, and (4) posterior to anterior (PA) fibular translation test. Distal tibiofibular coronal plane diastasis at the anterior and posterior third of syndesmosis, as well as AP and PA sagittal plane translation, were arthroscopically measured. RESULTS: The distal tibiofibular joint remained stable after transection of all lateral ankle ligaments (ATFL, CFL, and PTFL) as well as the AITFL. However, after additional transection of the IOL, the syndesmosis became unstable in both the coronal and sagittal plane. Syndesmosis laxity in the coronal plane was also observed after transection of the ATFL, CFL, AITFL, and IOL. Subsequent transection of the PITFL precipitated syndesmosis laxity in the sagittal plane, as well. CONCLUSIONS: The findings from the present study suggest that lateral ankle ligament injuries itself do not directly affect the stability of syndesmosis. However, if it combines with IOL injuries, even partial injuries cause syndesmotic laxity. As a clinical relevance, accurate diagnosis is the key for surgeons to determine syndesmosis fixation whether there is only AITFL injury or combined IOL injury in concomitant acute syndesmotic and lateral ligament injury.

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

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.

Novel values in the radiographic diagnosis of ligamentous Lisfranc injuries.

BACKGROUND: Ligamentous Lisfranc instability is commonly missed on unilateral radiographs. However, measurement protocols for bilateral weightbearing radiographs have not been standardized. The primary aim of this study was to investigate the optimal cut-off values for diagnosing Lisfranc instability by evaluating the side-to-side differences of preoperative bilateral weightbearing radiographs among patients with surgically-confirmed ligamentous Lisfranc instability. A secondary aim was to investigate whether the midfoot measurements for detecting Lisfranc injury could also be used in patients with a pre-existing bilateral Hallux Valgus (HV) deformity by evaluating whether the Lisfranc measurements could be affected by a foot deformity as HV. PATIENTS AND METHODS: Patients who underwent surgical repair of ligamentous Lisfranc instability, as well as a separate cohort with bilateral hallux valgus deformity, were included in this multicenter retrospective cohort study. A standardized radiographic measurement protocol was used to assess the midfoot and a receiver operator correlation (ROC) analysis was used to identify the optimal cut-off value for measurements. Interclass Correlation (ICC) scores were calculated to assess the interrater reliability of the Lisfranc area measurement. RESULTS: Forty-seven patients were included in the Lisfranc group with a mean age of 33 (± 15) years and 25 patients were included in the HV group with a mean age of 51 (± 15) years. For the Lisfranc group, measurements that demonstrated a significant side-to-side difference included; increased C1M2 diastasis of 2.4 mm (± 1.4, P<0.001), increased C1M2 surface area of 24 mm2 (± 15, P<0.001), C2M2 malignment by 1.7 mm (± 1.2, P<0.001), second tarsometatarsal joint dorsal step-off sign by 0.8 mm (± 0.7, P<0.001), and arch height by 2.5 mm (± 6.4, P<0.048), all greater on the injured side. In the HV group, side-to-side measurements were not significantly different. There was no significant difference comparing the M1M2 measurement in the HV group with the injured (P = 0.16) or uninjured (P = 0.08) foot in the Lisfranc group. The optimal cut-off points were between the injured and uninjured foot in the Lisfranc group were 2.1 mm for C1M2 diastasis, 0.7 mm for the C2M2 alignment, and 30 mm2 for the C1M2 surface area. The ICC-score for the second C1M2 area measurement was 0.88. CONCLUSION: Bilateral foot weightbearing radiographs can effectively diagnose ligamentous Lisfranc instability using a standardized measurement protocol. Malalignment of the medial aspect of the second metatarsal base ≥0.3 mm relatively to the intermediate cuneiform offers a high sensitivity, and distance ≥2.1 mm between the second metatarsal base and the medial cuneiform has a high specificity. Intermetatarsal distance between the first and second metatarsal base has a low sensitivity and specificity and should not be used in solitary for diagnosis. LEVEL OF EVIDENCE: Level III, retrospective comparative study.

Lisfranc injury: Refined diagnostic methodology using weightbearing and non-weightbearing radiographs.

BACKGROUND: To compare diagnostic parameters for Lisfranc instability on WB and NWB radiographs and to assess the inter-observer reliability of a standardized diagnostic protocol. PATIENTS AND METHODS: Patients who had undergone surgical treatment for subtle, purely ligamentous Lisfranc injury with both WB and NWB post-injury, pre-surgery films (n = 26) were included in this multicentre, retrospective comparative study. Also included was a control group (n = 26) of patients with isolated fifth metatarsal avulsion fractures who similarly had both WB and NWB films. Multiple midfoot distance and alignment measurements were used to evaluate the Lisfranc joint on both WB and NWB views. To evaluate interobserver reliability, measurements were made by two independent observers across a cohort subset. RESULTS: When comparing the NWB views between groups, only C1M2 (medial cuneiform- second metatarsal) distance was found to be significantly larger (∆ = 1.35 mm, p <0.001) for Lisfranc injuries. Most notably, C2M2 (Intermediate cuneiform - second metatarsal) step off-caused by lateral translation of the second metatarsal base-was not significantly different (∆ = 0.39 mm, p = 0.101) between Lisfranc patients and controls. On WB views, Lisfranc patients had significantly larger changes to C1M2 distance and C2M2 step-off as compared to controls (∆ = 2.97 mm, p <0.001 and ∆ =  1.98 mm, p <0.001 respectively). M1M2 (first to second intermetatarsal) distance was not significantly different between patients and controls in WB films. Within the cohort of ligamentous Lisfranc patients, C1M2 distance and C2M2 step-off were significantly larger in WB when compared to NWB films (∆ =  1.77 mm, p <0.001 and ∆ =  1.58 mm, p <0.001 respectively). For these parameters, inter-observer reliability scores (ICC) of >0. 90 were found when interpreting WB radiographs and ICC's ranging between 0.61 and 0.80 were found when interpreting NWB radiographs. CONCLUSION: Using WB imaging for diagnosing subtle Lisfranc instability reveals larger diastasis in the tarsometatarsal joint and has a higher interobserver reliability compared to NWB imaging. Clinical concern for subtle or occult Lisfranc instability in any patient should therefore trigger WB radiographic assessment since such injuries may be missed on NWB views.

Comparison of Several Combinations of Suture Tape Reinforcement and Suture Button Constructs for Fixation of Unstable Syndesmosis.

INTRODUCTION: The purpose of this study was to arthroscopically evaluate syndesmotic stability after fixation with several combinations of suture buttons (SBs) and suture tape reinforcement in a completely unstable cadaver model. METHODS: Fifteen cadaver above-knee specimens underwent sequential ligament transection and fixation to create six experimental models: (1) intact model, (2) after complete disruption of the syndesmotic ligaments, and after repair with either suture tape reinforcement (3), suture tape reinforcement with a single SB (4), suture tape reinforcement with two diverging SBs (5), or two diverging SBs alone (6). Instability measurements included anterior and posterior tibiofibular spaces measured arthroscopically under 100 N coronal stress, tibiofibular anteroposterior and posteroanterior translation in sagittal plane measured arthroscopically under sagittal stress of 100 N, and anterior tibiofibular space measured directly with a caliper under external rotation torque of 7.5 N·m. Instability measurements taken after each fixation method were compared with the uninjured model and with the complete unstable model using the Wilcoxon signed-rank test. RESULTS: Fixation using a combination of one SB and singular suture tape reinforcement augmentation provided stability similar to the intact stage (coronal anterior space 1.24 versus 1.15, P = 0.887; coronal posterior space 1.63 versus 1.64, P = 0.8421; anteroposterior translation 0.91 versus 0.46, P = 0.003; posteroanterior translation 0.51 versus 0.57, P = 0.051; external rotation anterior tibiofibular space 1.08 versus 0.55, P = 0.069). Moreover, adding a second SB led to further gains in fixation stability. DISCUSSION: This study suggests that although a destabilizing syndesmotic injury that includes the anterior inferior tibiofibular ligament, interosseous ligament, and posterior inferior tibiofibular ligament is not adequately stabilized by either one or two SBs, the addition of a suture tape reinforcement to even one SB restores syndesmotic stability to the preinjury level.