Abstract: A method for use of machine learning in computer-assisted anatomical prediction. The method includes identifying with a processor parameters in a plurality of training images to generate a training dataset, the training dataset having data linking the parameters to respective training images, training at least one machine learning algorithm based on the parameters in the training dataset and validating the trained machine learning algorithm, identifying with the processor digitized points on a plurality of anatomical landmarks in a radiographic image of a person's skeleton displayed on a screen by determining anatomical relationships of adjacent bony structures as well as dimensions of at least a portion of a body of the skeleton in the displayed image using the validated machine learning algorithm and a scale factor for the displayed image, and making an anatomical prediction of the person's skeletal alignment based on the determined anatomical dimensions and a known morphological relationship.

Abstract: A method for use of machine learning in computer-assisted anatomical prediction. The method includes identifying with a processor parameters in a plurality of training images to generate a training dataset, the training dataset having data linking the parameters to respective training images, training at least one machine learning algorithm based on the parameters in the training dataset and validating the trained machine learning algorithm, identifying with the processor digitized points on a plurality of anatomical landmarks in an image of a person displayed on a digital touch screen by determining linear anatomical dimensions of at least a portion of a body of the person in the displayed image using the validated machine learning algorithm and a scale factor for the displayed image, and making an anatomical circumferential prediction of the person based on the determined linear anatomical dimensions and a known morphological relationship.

Abstract: A method for use of machine learning in computer-assisted anatomical prediction. The method includes identifying with a processor parameters in a plurality of training images to generate a training dataset, the training dataset having data linking the parameters to respective training images, training at least one machine learning algorithm based on the parameters in the training dataset and validating the trained machine learning algorithm, identifying with the processor digitized points on a plurality of anatomical landmarks in a radiographic image of a person's skeleton displayed on a screen by determining anatomical relationships of adjacent bony structures as well as dimensions of at least a portion of a body of the skeleton in the displayed image using the validated machine learning algorithm and a scale factor for the displayed image, and making an anatomical prediction of the person's skeletal alignment based on the determined anatomical dimensions and a known morphological relationship.

Abstract: A method for use of machine learning in computer-assisted anatomical prediction. The method includes identifying with a processor parameters in a plurality of training images to generate a training dataset, the training dataset having data linking the parameters to respective training images, training at least one machine learning algorithm based on the parameters in the training dataset and validating the trained machine learning algorithm, identifying with the processor digitized points on a plurality of anatomical landmarks in an image of a person displayed on a digital touch screen by determining linear anatomical dimensions of at least a portion of a body of the person in the displayed image using the validated machine learning algorithm and a scale factor for the displayed image, and making an anatomical circumferential prediction of the person based on the determined linear anatomical dimensions and a known morphological relationship.

Abstract: A mobile, hand-held communication device with a digital touch screen display and a camera for acquiring an image of the human body is programmed to function as a digital anthropometer. The user digitizes anatomical landmarks on the displayed image to quickly obtain linear measurements which are used with a known morphological relationship to make an anatomical prediction for clothing measurement, body composition and postural displacement with accuracy and without external equipment.

Abstract: A mobile, hand-held communication device with a digital display, and a 3D camera for acquiring a three-dimensional image of the human body is programmed to function as a digital anthropometer. The user digitizes anatomical landmarks on the displayed image to quickly obtain anatomical measurements which are used with a known morphological relationship to make an anatomical prediction for clothing measurement, body composition, orthotic and insert manufacturing, and postural displacement with accuracy and without external equipment.

Abstract: A mobile, hand-held communication device with a digital touch screen display and a camera for acquiring an image of the human body is programmed to function as a digital anthropometer. The user digitizes anatomical landmarks on the displayed image to quickly obtain linear measurements which are used with a known morphological relationship to make an anatomical prediction for clothing measurement, body composition and postural displacement with accuracy and without external equipment.

Abstract: A mobile, hand-held communication device with a digital display, and a 3D camera for acquiring a three-dimensional image of the human body is programmed to function as a digital anthropometer. The user digitizes anatomical landmarks on the displayed image to quickly obtain anatomical measurements which are used with a known morphological relationship to make an anatomical prediction for clothing measurement, body composition, orthotic and insert manufacturing, and postural displacement with accuracy and without external equipment.

Abstract: A mobile, hand-held communication device with a display screen and a camera is programmed to perform a postural screening method. An image of a patient is acquired on the display screen having an array of pixels. A pixel to distance ratio of the displayed image is determined. A postural displacement of the patient in the displayed image is calculated using the determined ratio. A gyroscope, accelerometer and/or a level of the device is used to level the camera before capturing the image with the camera. Reference lines are overlaid on the display screen and the image to enable alignment of the image. A reference line on the display screen is used for normalizing a known distance in the aligned image to a reference distance of a known number of pixels on the screen for determining the ratio.

Abstract: A mobile, hand-held communication device with a digital touch screen display and a camera for acquiring an image of the human body is programmed to function as a digital anthropometer. The user digitizes anatomical landmarks on the displayed image to quickly obtain linear measurements which are used with a known morphological relationship to make an anatomical prediction for clothing measurement, body composition and postural displacement with accuracy and without external equipment.

Abstract: A mobile, hand-held communication device with a display screen and a camera is programmed to perform a postural screening method. An image of a patient is acquired on the display screen having an array of pixels. A pixel to distance ratio of the displayed image is determined. A postural displacement of the patient in the displayed image is calculated using the determined ratio. A gyroscope, accelerometer and/or a level of the device is used to level the camera before capturing the image with the camera. Reference lines are overlaid on the display screen and the image to enable alignment of the image. A reference line on the display screen is used for normalizing a known distance in the aligned image to a reference distance of a known number of pixels on the screen for determining the ratio.