A computer-implemented image analysis method, comprising: quantifying attributes of one or more features segmented and identified from a musculoskeletal medical image including bone of a subject; extracting non-image data pertaining to the subject from one or more non-image data sources ; extracting clinically relevant features from the non-image data pertaining to the subject; assessing bone fragility, comprising feeding the quantified attributes of the features segmented from the medical image into a trained machine learning model and assessing the quantified attributes of the features segmented from the medical image and the features extracted from the non-image data with the trained machine learning model ; and outputting one or more results of the assessing of the bone fragility; wherein the trained machine learning model comprises a model trained using features extracted from patient data and labels or annotations indicating disease or non-disease; and wherein the bone is a radius bone identified and segmented from a wrist, High-Resolution peripheral Quantitative Computed Tomography, HRpQT, scan using a pre-trained deep neural network model; and the radius bone is segmented into compact cortex , transitional zone , and trabecular region and the attributes of different structures or combination of structures are quantified into the following features: the volumetric bone mineral density of the entire radius bone , the percentages of pores in the bone volume , the volumetric bone mineral density of the transitional region , the volumetric bone mineral density of the trabecular region , the average percentage of the mineralized material , the density of marrow adiposity in the trabecular region , the average thickness of compact cortex , and the average separation of trabecular tissues . quantifying attributes of one or more features segmented and identified from a musculoskeletal medical image including bone of a subject; extracting non-image data pertaining to the subject from one or more non-image data sources ; extracting clinically relevant features from the non-image data pertaining to the subject; assessing bone fragility, comprising feeding the quantified attributes of the features segmented from the medical image into a trained machine learning model and assessing the quantified attributes of the features segmented from the medical image and the features extracted from the non-image data with the trained machine learning model ; and outputting one or more results of the assessing of the bone fragility; wherein the trained machine learning model comprises a model trained using features extracted from patient data and labels or annotations indicating disease or non-disease; and wherein the bone is a radius bone identified and segmented from a wrist, High-Resolution peripheral Quantitative Computed Tomography, HRpQT, scan using a pre-trained deep neural network model; and the radius bone is segmented into compact cortex , transitional zone , and trabecular region and the attributes of different structures or combination of structures are quantified into the following features: the volumetric bone mineral density of the entire radius bone , the percentages of pores in the bone volume , the volumetric bone mineral density of the transitional region , the volumetric bone mineral density of the trabecular region , the average percentage of the mineralized material , the density of marrow adiposity in the trabecular region , the average thickness of compact cortex , and the average separation of trabecular tissues . <EOS>