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Category: Hindfoot; Ankle Introduction/Purpose: Tibiotalocalcaneal (TTC) arthrodesis is a complex procedure for severe ankle and hindfoot pathologies like end-stage osteoarthritis or avascular necrosis of the talus. Proper calcaneal fixation with the talus and tibia is crucial for postoperative stability and neurovascular protection in intramedullary nailing. A cadaveric study by Hyer et al. showed that a wire placed anterograde through the tibial shaft may miss the calcaneus medially. Methodologic limitations included a limited number of cadavers, non-loaded specimens, and a lack of critical examination of hindfoot alignment. This study aims to determine the feasibility of using weightbearing computed tomography (WBCT) and 3D segmentation software to ascertain an optimal entry point for a straight intramedullary nail in TTC arthrodesis in weightbearing patients. Methods: A total of 100 normal ankles (48 left, 52 right) from 52 patients (Age: 39.9 ± 16.7 years; Gender: 68% F; BMI: 28.1 ± 5.8 kg/m^2) with WBCT scans were obtained and subsequently segmented for inclusion in the study. A novel mathematical algorithm was developed to determine the 3D intersection of the distal tibial central axis extension at the inferior calcaneus. The algorithm approximates the central axis by mapping the center point of the tibial medulla at each CT slice, from the shaft to the tibiotalar joint using 3D surface contours in the axial plane. Then, the central axis is extended through the talus and calcaneus to approximate its exit point on the inferior surface of the calcaneus for each individual ankle (Figure 1a). A 2D linear transformation (affine transformation) was applied to obtain a heat map of all intersection points on the inferior surface of the calcaneus. Results: The mean (SD) polar angle of the central axis with respect to the ground was 84.9 ± 2.3 degrees. The root mean (SD) square error of the affine transformation was 1.62 ± 1.02 mm, demonstrating the validity of mapping. The exit points of the central axis across all patients fall predominantly along the sustentaculum tali (Figure 1b). Conclusion: A straight nail passing through the sustentaculum tali places surrounding neurovascular structures at risk and can also compromise the biomechanical stability of the implant. Our novel 3D algorithm shows that intraoperative adjustment of the starting point lateral to the central tibial axis is required to obtain optimal alignment of the hindfoot nail. Additionally, the novel algorithm can also be used preoperatively to estimate the required calcaneal medialization and ensure precise implant positioning. Proper nail alignment may potentially improve the success of TTC arthrodesis and decrease the risk of postoperative complications, including implant failure.

Category: Midfoot/Forefoot; Trauma Introduction/Purpose: The diagnosis of fractures can be challenging in specific medical settings due to limited expertise or time. While deep learning has shown promising results, its use is confined to the quality of images and the hassle of importing images to the models. This study aims to develop a model to detect fifth metatarsal fractures based on cell phone photos of radiographs directly taken from a regular screen. Methods: retrospective case-control study was conducted including patients aged > 18 years with fifth metatarsal fractures (n=1240) (Fx), and healthy controls (n=1224) (NoF). Three view radiographs (anterior, posterior, and lateral) were obtained from the Electronic Health Record (EHR) in PNG format. To generate a mixed-quality dataset, Android and iOS smartphones (SP) were used to create two separate datasets for each Fx and NoF group. Two separate deep learning models on each EHR, SP, and combined datasets were developed using Inception V3 architecture (Figure 1.). The models were also tested on a separate SP dataset (SP-test) that was not included in the development process. Area Under the Receiver Operating Characteristics Curve (AUC) along with other performance metrics were calculated and reported. Continuous data were presented as median (interquartile range), and a p-value of < 0.05 was considered to be significant. Results: Baseline analysis revealed differences between the groups with a median age of 56 years (36-68) for the Fx group, and 62 years (51-72) for the NoF group (p < 0.001). Similarly, the racial composition of the groups was also different (Fx: 84.8% white; NoF: 92.2% white; p < 0.001). Initially, the SP model showed the best performance (Youden Index (YI): 0.92, AUC: 0.99) followed by the EHR (YI: 0.74, AUC: 0.96), and combined (YI: 0.52, AUC: 0.97) models. When tested on the SP-test dataset the EHR model’s performance dropped markedly showing a YI of 0.33, an AUC of 0.78, and a sensitivity of 0.49 (Table 1.). However, the SP and combined models continued to perform optimally (YI: 0.94, AUC: 0.99; YI: 0.78, AUC: 0.98, respectively). Conclusion: This study highlights the crucial role of image quality in developing deep learning models for detecting fifth metatarsal fractures. Our findings demonstrate a markedly reduced performance of the EHR model in identifying fractures within lower-quality images. This emphasizes the need for training algorithms on images of varying quality to create more generalizable models capable of operating effectively across diverse settings.

Category: Midfoot/Forefoot; Hindfoot Introduction/Purpose: Arch-support insoles are frequently used as part of the treatment protocol for a variety of common foot ailments including metatarsalgia, and plantar fasciitis. While custom-molded insoles are considered to be the most efficacious ones, prefabricated insoles are still preferred due to their lower price and availability. However, there's ongoing skepticism about their capacity to meet the diverse and changing biomechanical requirements of every individual over time. In this study, we investigated the effect of a patient-specific, three-level-graded arch support system for the treatment of common foot ailments using patient-reported outcome measures of pain. Methods: A single-armed clinical trial was conducted including patients aged >18 years with a diagnosis of plantar fasciitis or metatarsalgia. Based on initial measurements, each participant was given three specific arch support orthotics (Good Feet™, Dr.’s Own, LLC) with varying arch heights, to be worn at tolerance, along with a pair of sneakers for standardization of the footwear condition. Symptomatic relief was measured at the baseline-, fourth-, eighth-, and twelfth-week appointments using the Patient Reported Outcomes Measurement System (PROMIS) for pain Intensity, pain Interference, and the American Orthopaedic Foot and Ankle Society (AOFAS) questionnaire. Moreover, the structural changes of the foot were also measured using Weight-bearing Computed Tomography (WBCT) scans, and X-rays of the foot with and without the insoles at the initial visit. Data were analyzed employing the T-test, Wilcoxon Signed Rank, and Friedman tests where applicable. A p-value below 0.05 was considered to be statistically significant. Results: A total of 43 patients with a mean (SD) age of 41.69 (12.21) years, a female-to-male ratio of 1.3:1, and a body mass index (SD) of 25 (4.04) were included in the study. PROMIS pain interference and intensity scores improved significantly starting from the fourth week (p=0.017, and 0.003, respectively). Additionally, the AOFAS questionnaire demonstrated enhanced scores notably on the lesser metatarsophalangeal-interphalangeal, and midfoot subscales with p-values of 0.001, and < 0.001, respectively (Figure 1.). Moreover, the navicular height on WBCT and the talo-navicular angle on lateral view X-rays increased when the insoles were utilized (p=0.046, and < 0.001, respectively; Figure 2.). Conclusion: This study revealed the significance of prefabricated graded arch supports for the symptomatic relief of patients with plantar fasciitis and metatarsalgia. These clinical improvements were also supported by discernable anatomical changes as evidenced by foot radiography and WBCT assessments. Given these findings, we believe future research should be focused on evaluating the efficacy of these insoles in managing additional foot pathologies. Moreover, it is imperative to explore the biomechanical ramifications of such supports on the dynamic alignment of the foot, to further understand their role in altering foot structure and function.

Category: Hindfoot; Trauma Introduction/Purpose: Fractures of the calcaneus are the most frequent form of fractures among the tarsal bones. The treatment plan depends on various fracture characteristics, particularly the extension of the fracture line into the subtalar joint surfaces. While X-rays are the primary diagnostic tool, Computed Tomography (CT) scans are often necessary as they provide a more detailed description of these fractures, especially if the articular surfaces are involved. Although informative, CT scans expose patients to higher doses of ionizing radiation, and they might not be readily available in certain settings. This study aims to develop a deep learning model that will first detect calcaneal fractures and then classify the type of fracture based on its extension into the subtalar joint surfaces. Methods: A retrospective case-control study was conducted. Patients aged >18 years who sustained a calcaneal fracture were included in the case group, along with the same number of healthy adults for the control group. Various X-ray views of the foot including anterior, posterior, lateral, along with the axial and lateral calcaneal views were obtained. Two experienced orthopaedic researchers screened the dataset and confirmed the type of fracture. Two models were developed using an Inception V3 architecture and a split ratio of 60:20:20 for training, validation, and testing purposes. The first model was designed for fracture detection, and the second one was designed for detecting intra-articular fracture extension, if present. Conventional model metrics including sensitivity, specificity, Youden index, area under the receiver operating characteristic curve (AUC), and F1 score were reported for each model. A p-value below 0.05 was statistically significant. Baseline values are presented in median and interquartile ranges (IQR). Results: A total of 1102 individuals (n=551 for each group) with a female-to-male ratio of 1.6:1 were included. Baseline comparisons showed a significant difference in the age of the groups [cases: 55 (IQR 42, 67) years; controls: 62 (IQR 51, 72 years), p-value < 0.001]. The first model for fracture detection demonstrated a high performance with an AUC of 0.99 and a Youden index of 0.96. The intra-articular fracture detection model showed a slightly lower performance with an AUC of 0.97 and a Youden index of 0.88 (Table 1, Figure 1). Conclusion: Our study showed promising results with the utility of deep learning methods for detecting calcaneal fractures and classifying intra- vs extra-articular fractures. This algorithm may help facilitate the diagnosis and treatment of patients with suspected calcaneal fractures, serving as a decision-support tool in settings where CT scanning is not available.

Category: Hindfoot; Other Introduction/Purpose: Plantar fasciitis (PF), a leading cause of persistent heel pain, results in almost a million physician visits annually. Conservative treatment is often the first line of management with insoles being frequently prescribed. While multiple studies have compared insoles based on the degree of customization to foot contour, the literature is lacking in data comparing insoles based on their material. In this randomized clinical trial, we compared the early effects of foam, polyurethane, and carbon fiber insoles in the treatment of PF, using a robust set of PROMS. Methods: A randomized clinical trial was designed at the foot and ankle research center of a tertiary care hospital in Massachusetts. Adult patients diagnosed with PF who had not received injectable or surgical treatment for it were included. Participants who consented were randomly allocated one of the three prefabricated insoles – carbon fiber insole (Group 1, n=13), polyurethane insole (Group 2, n=13), or foam insole (Group 3, n=9) for regular use. Their response was recorded using PROMIS 3a (for pain intensity), PROMIS 4a (for pain interference), FAOS (Foot and Ankle Outcome Score), and VAS for pain at baseline (T0), two (T2), six (T6) and twelve weeks (T12). A per-proto analysis was undertaken using Kruskal Wallis and Friedman tests. P< 0.05 was set as the level of significance. Results: Among groups 1, 2 and 3, the mean age of participants was 51.9±13.2, 51±12.5, and 47.9±13.5 years respectively and baseline weight varied as 211.6±32.7 lbs, 193.6±59.3 lbs, and 180.7±33 lbs respectively (p>0.05 for both). The group-wise distribution of participants based on sex and laterality of the affected foot showed no difference. Participants in each group reported similar intensity of the pain (VAS) at baseline and at all follow-up time points (p>0.05). A comparison of outcomes between baseline and subsequent timepoints showed significant improvement in quality of life (FAOS) at T12 (p=0.047) for carbon fibre insole users (Figure 1), significant pain relief on PROMIS Pain intensity and Pain Interference questionnaires for foam insoles at T3 and T12 (p=0.0003, 0.036 respectively). Conclusion: Carbon fiber insoles offered slightly better pain relief as early as six weeks into treatment and improved quality of life after twelve weeks of use. A possible link between the material of insole used and effective treatment of PF may exist. Conducting further analyses on the position of the foot during gait while using insoles and designing the insoles based on patient-specific criteria should be considered for future research.

Category: Midfoot/Forefoot; Lesser Toes Introduction/Purpose: Arch-support insoles are frequently included in the treatment plans for common foot ailments including metatarsalgia. Literature has demonstrated that insoles with metatarsal and arch support could relieve walking pain and improve patient-reported measures of function in metatarsalgia. The purpose of this study was to examine alterations in foot alignment among metatarsalgia patients who used arch support insoles. Methods: A clinical trial was initiated after the approval by the institutional review board. Patients with metatarsalgia (age: 18-65 y/o) were included after they consented to participate. Individuals with open wounds, feet asymmetry, using assistive device or brace, and those with BMI more than 35 were excluded. Participants underwent weightbearing computed tomography (WBCT), and weight-bearing x-ray of their feet while standing barefoot or on the insoles (Good Feet™, Dr.’s Own, LLC). The radiological measurements on WBCT and X-rays conducted in these patients are shown in Table 1. The Wilcoxon-Signed Rank test was used for comparison of the continuous measurements, and the interobserver reliability was analyzed with Intraclass Correlation Coefficient (ICC). Results: Ten patients with a mean age of 46.9±13.06 years were included in the study. Observed changes on X-rays include decreased 4th-5th intermetatarsal angle (p=0.04), 2nd-4th/2nd-5th metatarsal tangent angles (p=0.003, p=0.001), and 1st metatarsal length on antroposterior (AP) view (p=0.02). Also, 1st metatarsal declination angle (p=0.002), and talo-first metatarsal angle on AP view (p=0.05) were increased. No significant changes on the WBCT were found, except for a decrease in the first metatarsal pronation angle (p=0.02). Conclusion: Arch support insoles can bring about anatomical changes especially in the forefoot area of patients with metatarsalgia. While the causal correlation between these changes and alleviation of the symptoms cannot be proven based on our data, these outcomes can guide future clinical trials comparing different treatment for metatarsalgia to determine contributing factors to the healing process of this condition.

Category: Ankle; Other Introduction/Purpose: Anatomical reduction of the distal syndesmosis can be challenging. There is ongoing debate and variability in the methods used for evaluating the accuracy of reduction, including radiography, intra-operative CT, arthroscopy, and direct visualization. Tornetta et al. have described a method called ‘the articular surface method’ that evaluates the relationship between the articular cartilage of the distal anteromedial fibula and the anterolateral tibia as being significantly more accurate for detecting malreduction. However, it entails an additional surgical incision over the distal aspect of the ankle. The aim of this study was to find a non-invasive method using ultrasound to assess the accuracy of reduction in syndesmotic injuries. Methods: A cadaveric syndesmotic instability model was created by dissecting the PITFL, IOL, and AITFL through a small posterior incision. The fibula was fixed in incremental degrees of rotational malreduction to achieve a malreduction of 3, 5, and 7 mm. A blinded observer assessed the syndesmosis using a portable ultrasound device. The probe was placed in its short axis at the level of the ankle joint then moved proximally until both the anteromedial fibular and anterolateral tibial articular surfaces were visualized simultaneously in one view. In a reduced syndesmosis, the distal articular surfaces of the tibia and fibula overlap. This relationship is altered in a malreduced syndesmosis, which allows ultrasonographic waves to ‘penetrate’ through the malreduced articular surfaces and be readily detected. This sign was graded positive if an acoustic signal penetrated between the distal articular surfaces of the tibia and fibula, and negative if no acoustic signal was detected. Results: The gap penetrance sign was positive in all 3 instances of malreduced syndesmoses, and negative in an anatomically reduced syndesmosis. Figure 1 illustrates the outcomes in a reduced syndesmosis and malreduced syndesmosis, respectively. Conclusion: We introduced a novel sign that can be used as a surrogate of the ‘articular surface method’ to detect syndesmotic malreduction. It is accurate, can obviate the need for a separate surgical incision for direct visualization, permits rapid point-of- care evaluation in the operating room, and minimizes radiation exposure