俗話說，眼見為實(shí)。慢性病的治愈過程其實(shí)很難看見，努力練習(xí)跑更快跳更遠(yuǎn)的進(jìn)步往往也不明顯。

但是，如果有人工智能幫忙呢？

根據(jù)醫(yī)學(xué)期刊《IEEE Transactions on Medical Imaging》新發(fā)表的一項(xiàng)研究，位于大阪附近的奈良科學(xué)技術(shù)學(xué)院（Nara Institute of Science and Technology）的研究人員稱，已經(jīng)開發(fā)出“深度學(xué)習(xí)”人工智能工具，能夠更好地區(qū)分單塊肌肉，更快速準(zhǔn)確搭建個(gè)人肌肉骨骼系統(tǒng)的模型。醫(yī)學(xué)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(dǎo)該項(xiàng)研究的奈良科學(xué)技術(shù)學(xué)院的教授佐藤義雄在一份聲明中說：“細(xì)分很耗時(shí)，而且需要專業(yè)知識(shí)。我們使用深度學(xué)習(xí)技術(shù)自動(dòng)細(xì)分單獨(dú)的肌肉，為每位患者生成個(gè)性化的肌肉骨骼模型。”

“深度學(xué)習(xí)”是人工智能研究領(lǐng)域的術(shù)語(yǔ)，主要利用所謂的神經(jīng)網(wǎng)絡(luò)和龐大的計(jì)算能力，通過實(shí)例學(xué)習(xí)并模仿人類學(xué)習(xí)的方式。

在研究過程中，奈良科學(xué)技術(shù)學(xué)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(xì)分肌肉時(shí)能否超過傳統(tǒng)的成像方法（包括公認(rèn)最新技術(shù)的分層多圖譜分割）。最后新工具成功了，也縮短了外科醫(yī)生調(diào)試及驗(yàn)證系統(tǒng)的時(shí)間。

該研究由研究員們與大阪大學(xué)醫(yī)院合作進(jìn)行。

該工具潛在的應(yīng)用方式眾多。一方面能夠讓醫(yī)療服務(wù)提供方開發(fā)更有效的康復(fù)設(shè)備，幫助患有肌萎縮側(cè)索硬化（亦被稱為ALS）及其它導(dǎo)致肌肉嚴(yán)重萎縮疾病的患者。另一方面，全球的頂級(jí)運(yùn)動(dòng)員也可以借此更好地了解自身生物力學(xué)。

本次突破只是個(gè)性化醫(yī)療廣闊領(lǐng)域的眾多突破之一，由研究人員在名叫Bayesian U-Net的深度學(xué)習(xí)框架上完成。個(gè)性化醫(yī)療通常使用個(gè)人數(shù)據(jù)為特定患者量身定制治療方案，不再選擇針對(duì)普通人群的常規(guī)治療。

如果利用該工具審查醫(yī)學(xué)圖像，將來(lái)會(huì)不會(huì)替代行醫(yī)經(jīng)驗(yàn)豐富的骨科醫(yī)生？可能性不大。但該工具也許可以減少骨科醫(yī)生看圖像的時(shí)間，留出更多的時(shí)間實(shí)施診治。（財(cái)富中文網(wǎng)）

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.