Foot & ankle orthopaedics · 2025
Subtalar Joint Statistical Shape Modeling Differentiates Cavus-to-Planus Foot Types From Weightbearing CT.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Lapins E Renae, Peterson Andrew C, Saltzman Charles L, Elhabian Shireen Y, Chrea Bopha, Miyamoto Takuma, Lenz Amy L
Аннотация
BACKGROUND: Foot type significantly impacts the development and progression of foot and ankle pathologies by influencing biomechanics and force distribution. However, it is typically assessed qualitatively and with a 2D radiographic measurement called Meary's angle. This study seeks to determine the minimum number of bones required in a statistical shape model (SSM) to accurately represent the full cavus through planus spectrum, enabling future machine learning applications in clinical practice.
METHODS: Our study included weightbearing computed tomography (WBCT) data from 151 patients grouped based on clinical diagnosis or Meary's angle: 33 Charcot-Marie-Tooth (CMT), 29 cavus, 28 rectus, 27 planus, and 34 progressive collapsing foot deformity (PCFD). Ten multi-bone SSMs, with varying numbers of bones, were created from bony segmentations. Principal component analysis (PCA) assessed the modes of variation for all SSMs.
RESULTS: PCA mode 1 demonstrated significant results (α = 0.05) for all SSMs, with the 2-bone subtalar joint (STJ) model capturing the most variance at 70.9% and the largest effect size of 0.75. The mean shape of all SSMs exhibited neutral STJ and midfoot alignment, whereas severe cavus and planus deformities were observed at 2 SDs from the mean shape. Models that included the STJ had statistical differences between all group PCA score comparisons. In contrast, models without the STJ had significant differences between all groups, except between the planus and rectus groups.
CONCLUSION: STJ orientation and morphology appear fundamental for determining foot type. Our study revealed that the STJ alone offers sufficient information for computational differentiation because of its high variance and effect sizes when included in SSMs, highlighting the possible clinical utility as a simplified model. Although full foot models provide additional insights, the STJ model's effectiveness makes it ideal for streamlined assessment and treatment planning.
CLINICAL RELEVANCE: Modeling the STJ captures the full cavus-planus foot type spectrum, suggesting its morphology may drive foot type and related pathologies. This underscores the potential of using simplified models in combination with machine learning as a rapid morphologic classifier; clinical impact remains to be determined.
METHODS: Our study included weightbearing computed tomography (WBCT) data from 151 patients grouped based on clinical diagnosis or Meary's angle: 33 Charcot-Marie-Tooth (CMT), 29 cavus, 28 rectus, 27 planus, and 34 progressive collapsing foot deformity (PCFD). Ten multi-bone SSMs, with varying numbers of bones, were created from bony segmentations. Principal component analysis (PCA) assessed the modes of variation for all SSMs.
RESULTS: PCA mode 1 demonstrated significant results (α = 0.05) for all SSMs, with the 2-bone subtalar joint (STJ) model capturing the most variance at 70.9% and the largest effect size of 0.75. The mean shape of all SSMs exhibited neutral STJ and midfoot alignment, whereas severe cavus and planus deformities were observed at 2 SDs from the mean shape. Models that included the STJ had statistical differences between all group PCA score comparisons. In contrast, models without the STJ had significant differences between all groups, except between the planus and rectus groups.
CONCLUSION: STJ orientation and morphology appear fundamental for determining foot type. Our study revealed that the STJ alone offers sufficient information for computational differentiation because of its high variance and effect sizes when included in SSMs, highlighting the possible clinical utility as a simplified model. Although full foot models provide additional insights, the STJ model's effectiveness makes it ideal for streamlined assessment and treatment planning.
CLINICAL RELEVANCE: Modeling the STJ captures the full cavus-planus foot type spectrum, suggesting its morphology may drive foot type and related pathologies. This underscores the potential of using simplified models in combination with machine learning as a rapid morphologic classifier; clinical impact remains to be determined.