俗話說，眼見為實。慢性病的治愈過程其實很難看見，努力練習跑更快跳更遠的進步往往也不明顯。

但是，如果有人工智能幫忙呢？

根據醫學期刊《IEEE Transactions on Medical Imaging》新發表的一項研究，位于大阪附近的奈良科學技術學院（Nara Institute of Science and Technology）的研究人員稱，已經開發出“深度學習”人工智能工具，能夠更好地區分單塊肌肉，更快速準確搭建個人肌肉骨骼系統的模型。醫學專家可以使用該模型研究人體肌肉和骨骼的力量及承受的壓力。

主導該項研究的奈良科學技術學院的教授佐藤義雄在一份聲明中說：“細分很耗時，而且需要專業知識。我們使用深度學習技術自動細分單獨的肌肉，為每位患者生成個性化的肌肉骨骼模型。”

“深度學習”是人工智能研究領域的術語，主要利用所謂的神經網絡和龐大的計算能力，通過實例學習并模仿人類學習的方式。

在研究過程中，奈良科學技術學院的研究人員利用工具觀察人體大腿和臀部的19塊肌肉，主要看細分肌肉時能否超過傳統的成像方法（包括公認最新技術的分層多圖譜分割）。最后新工具成功了，也縮短了外科醫生調試及驗證系統的時間。

該研究由研究員們與大阪大學醫院合作進行。

該工具潛在的應用方式眾多。一方面能夠讓醫療服務提供方開發更有效的康復設備，幫助患有肌萎縮側索硬化（亦被稱為ALS）及其它導致肌肉嚴重萎縮疾病的患者。另一方面，全球的頂級運動員也可以借此更好地了解自身生物力學。

本次突破只是個性化醫療廣闊領域的眾多突破之一，由研究人員在名叫Bayesian U-Net的深度學習框架上完成。個性化醫療通常使用個人數據為特定患者量身定制治療方案，不再選擇針對普通人群的常規治療。

如果利用該工具審查醫學圖像，將來會不會替代行醫經驗豐富的骨科醫生？可能性不大。但該工具也許可以減少骨科醫生看圖像的時間，留出更多的時間實施診治。（財富中文網）

譯者：馮豐

審校：夏林

As the saying goes, seeing is believing—sometimes in a cure for a chronic disease, sometimes in the opportunity to run faster or jump farther than ever before.

But what if artificial intelligence can assist?

According to a newly published study in the medical journal IEEE Transactions on Medical Imaging, researchers at the Nara Institute of Science and Technology, known as NAIST and located outside of Osaka, say they have developed a “deep learning” A.I. tool that allows them to better tell apart, or “segment,” individual muscles. The tool ultimately allows for the faster creation of a more accurate model of a person’s musculoskeletal system, which medical professionals then use to study the forces and stresses on muscles and bones.

“This segmentation was time consuming and depended on expert-knowledge,” said Yoshinobu Sato, the NAIST professor who led the study, in a statement. “We used deep learning to automate the segmentation of individual muscles to generate a musculoskeletal model that is personalized to the patient.”

“Deep learning” is the term for the area of A.I. research that uses so-called neural networks—and a tremendous amount of computational horsepower—to learn by example and mimic the way humans learn.

For the study, the NAIST researchers directed the tool at 19 muscles in the thigh and hips to see if it could tell them apart better than conventional imaging methods, including hierarchical multi-atlas segmentation, considered state-of-the-art. It succeeded, even as it reduced the time a surgeon needs to train and validate the system.

Researchers conducted the study in collaboration with Osaka University Hospital.

The potential applications of the tool are numerous. It can help those who suffer from amyotrophic lateral sclerosis, known as ALS, and other disorders that result in severe muscle atrophy by allowing medical providers to develop more effective rehabilitation devices. It can also help the world’s top athletes who want to better understand their biomechanics.

The advancement, which the researchers built on a deep learning framework known as Bayesian U-Net, is just one of many in the broad area of health care known as personalized medicine, which involves using personal data to tailor treatment to the specific patient, rather than opting for conventional treatment that best targets the average population.

Will the tool replace the highly skilled orthopedic surgeons once needed to examine such medical images? It’s not likely. But it might just let them spend less time seeing and more time doing.