# Measurement of Lower Extremity Alignment Using a Smartphone Application
## 1 Introduction  
Knee osteoarthritis is a degenerative disease associated with factors such as age, trauma, obesity, and lower extremity malalignment; increased prevalence may be due to additional factors, including economic development, changes in lifestyle, and increase in sports activities [1, 2, 3]. Specifically, alignment of the lower extremities plays a crucial role in the efficient use and preservation of energy in the musculoskeletal system [1, 2, 3], and is important for older as well as young adults [4, 5, 6, 7, 8] because proper alignment supports the body's weight, aids in walking, and plays an important biomechanical role [4, 5, 6]. The axes of the hip, knee, and ankle help distribute appropriate loads towards the kneejoint. However, in cases of knee joint deformities, such as v to an imaginary vertical line between the two lower extremities. We obtained a formula using simple linear regression analysis with the actual alignment angle measured on full-length lower extremity radiographs. The obtained regression formula was as follows: (R2) = 80.4%, Y = \(a+\beta\)X was Y = \(-27.24+27.39\)X (Figure 1).  
This formula was then used to develop an application to measure varus or valgus angles for predicting the lower extremity alignment angle using photos captured on a smartphone. The application was developed using an Android-based software develop  
Figure 1: Measurement of lower extremity alignment on the anatomical skin surface. Points are marked on the thinnest part (inflection point) of the skin on the inner and outer sides of the ankle and knee, as shown on the X-ray image. The inner and outer points of the skin around the ankle (C2) and knee (C1) are connected at an intermediate value at a similar location on the other leg at the intermediate value of that point. A line perpendicular to the median value of the line between the ground and the left and right ankle is drawn. The distance from the vertical line to the median value of the left (D2) and right (D3) knee is then calculated. The ratio of D2 divided by half of D1 is the bending ratio of the left lower extremity, whereas the ratio of D3 divided by half of D1 is the ratio of the right lower extremity to its bent status. The linear regression formula calculated in a previous study was Y = \(-27.24+27.39\)X, where Y is the PLEAA and X is the bending ratio of the ankle and knee.  
ment kit and an application program interface. Based on PostNet, which is an open-source software provided by Google TensorFlow (Alphabet Inc., Mountain View, CA, USA), the algorithm derives images by outlining the entire body for human system measurement and subsequently designating specific physical points to calculate the predicted length and correlation between skeletal joints based on the images of the patient's lower extremities. Through this algorithm, an image was uploaded to MobileNet (Alphabet Inc.) based on PostNet, and the patient's posture was estimated using the Keypoint (Alphabet Inc.) heatmap and offset vector. The knee and ankle joints were extracted from the feature points and finally outputed to PostNet, and the final region of interest was then extracted. The final region of interest on the application predicts the lower extremity alignment angle by applying the results of the analysis to the recovery analysis formula of the previous studies after extracting the thinnest part of the skin around the knee and ankle joints [23]. The program was developed by the researchers and optimized for Android 8.0 version smartphones (Galaxy S8 SM-G950N, Samsung, Seoul, South Korea).