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.