Background and purpose — Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs.Methods — We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd’s Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network’s performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network.Results — All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen’s kappa under these conditions was 0.76.Interpretation — This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics.

The present update on the global distribution of Mycobacterium tuberculosis complex spoligotypes provides both the octal and binary descriptions of the spoligotypes for M. tuberculosis complex, including Mycobacterium bovis, from >90 countries (13,008 patterns grouped into 813 shared types containing 11,708 isolates and 1,300 orphan patterns). A number of potential indices were developed to summarize the information on the biogeographical specificity of a given shared type, as well as its geographical spreading (matching code and spreading index, respectively). To facilitate the analysis of hundreds of spoligotypes each made up of a binary succession of 43 bits of information, a number of major and minor visual rules were also defined. A total of six major rules (A to F) with the precise description of the extra missing spacers (minor rules) were used to define 36 major clades (or families) of M. tuberculosis. Some major clades identified were the East African-Indian (EAI) clade, the Beijing clade, the Haarlem clade, the Latin American and Mediterranean (LAM) clade, the Central Asian (CAS) clade, a European clade of IS6110 low banders (X; highly prevalent in the United States and United

We present a short summary of recent observations on the global distribution of the major clades of the Mycobacterium tuberculosis complex, the causative agent of tuberculosis. This global distribution was defined by data-mining of an international spoligotyping database, SpolDB3. This database contains 11,708 patterns from as many clinical isolates originating from more than 90 countries. The 11,708 spoligotypes were clustered into 813 shared types. A total of 1,300 orphan patterns (clinical isolates showing a unique spoligotype) were also detected.

No abstract