Osseointegration (OI) is the direct anchorage of a metal implant into bone, allowing for the connection of an external prosthesis to the skeleton. Osseointegration was first discovered in the 1960s based on the microscopic analysis of titanium implant placed into host bone. New bone was observed to attach directly to the metal surface. Following clinical investigations into dentistry applications, OI was adapted to treat extremity amputations. These bone anchored implants, which penetrate the skin and soft tissues, eliminate many of the challenges of conventional prosthetic sockets, such as poor fit and suspension, skin breakdown, and pain. Osseointegrated implants show promise to improve prosthesis use, pain, and function for amputees. The successful process of transcutaneous metal integration into host bone requires three synergistic systems: the host bone, the metal implant, and the skin‐implant interface. All three systems must be optimized for successful incorporation and longevity of the implant. Osseointegration begins during surgical implantation of the metal components through a complex interplay of cellular mechanisms. While implants can vary in design—including the original screw, press fit implants, and compressive osseointegration—they face common challenges to successful integration and maintenance of fixation within the host bone. Overcoming these challenges requires the understanding of the complex interactions between each element of OI. This review outlines (a) the basic components of OI, (b) the science behind both the bone‐implant and the skin‐implant interfaces, (c) the current challenges of OI, and (d) future opportunities within the field.

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Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost‐effective solution. Using open‐source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open‐source methods and externally validated in two unique patient populations. The training set of a well‐characterized, database records of 189 patients and the bnlearn package within R Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18, and 24 months. Each was externally validated in both a Scandinavian (n = 815 patients) and a Japanese (n = 261 patients) data set. Brier scores and receiver operating characteristic curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian data set. For the 1‐month time point, DCA of the Japanese data set suggested to expect better outcomes assuming all patients will survive greater than 1 month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance: we successfully transitioned to PATHFx 2.0 using open‐source software and externally validated it in two unique patient populations, which can be used as a cost‐effective option to guide surgical decisions in patients with metastatic bone disease.