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        人工智能在胰腺疾病診斷及治療中的應(yīng)用及展望

        2020-08-21 08:52:27郭秀鏐丁鶯徐秋萍
        中國(guó)現(xiàn)代醫(yī)生 2020年17期
        關(guān)鍵詞:人工神經(jīng)網(wǎng)絡(luò)機(jī)器學(xué)習(xí)胰腺癌

        郭秀鏐 丁鶯 徐秋萍

        [摘要] 人工智能是計(jì)算機(jī)科學(xué)的一個(gè)分支,是一門新的技術(shù)科學(xué)。以強(qiáng)大的計(jì)算和學(xué)習(xí)能力而廣泛應(yīng)用于臨床實(shí)踐的各個(gè)領(lǐng)域。本文回顧了人工智能在胰腺疾病診斷及治療中的應(yīng)用,特別是在急性胰腺炎的嚴(yán)重程度及預(yù)后評(píng)估、胰腺癌的診斷和預(yù)后等方面。然而人工智能是以“大數(shù)據(jù)”為基礎(chǔ)的,多中心數(shù)據(jù)庫(kù)的建立仍需要我們進(jìn)一步努力。此外,隨著人工胰腺在糖尿病應(yīng)用中的普及,人機(jī)關(guān)系在醫(yī)療實(shí)踐中占的比重也會(huì)越來(lái)越大。人工智能技術(shù)將會(huì)給臨床診療活動(dòng)帶來(lái)更多的便利。

        [關(guān)鍵詞] 人工智能;胰腺疾病;人工神經(jīng)網(wǎng)絡(luò);胰腺炎;胰腺癌;機(jī)器學(xué)習(xí)

        [中圖分類號(hào)] R57;R-05? ? ? ? ? [文獻(xiàn)標(biāo)識(shí)碼] A? ? ? ? ? [文章編號(hào)] 1673-9701(2020)17-0188-05

        Application and prospect of artificial intelligence in diagnosis and treatment of pancreatic diseases

        GUO Xiuliu? ? DING Ying? ? XU Qiuping

        Zhejiang University School of Medicine,Hangzhou? ?310020,China

        [Abstract] Artificial intelligence is a branch of computer science and a new technical science. It is widely used in various fields of clinical practice with strong computing and learning capabilities. This article reviews the application of artificial intelligence in the diagnosis and treatment of pancreatic diseases, especially in the assessment of the severity and prognosis of acute pancreatitis, and the diagnosis and prognosis of pancreatic cancer. However, artificial intelligence is based on "big data", and the establishment of a multi-center database still requires our further efforts. In addition, with the popularization of artificial pancreas in the application of diabetes, human-machine relationship will also become more and more important in medical practice. Artificial intelligence technology will bring more convenience to clinical diagnosis and treatment activities.

        [Key words] Artificial intelligence;Pancreatic diseases;Artificial neural network;Pancreatitis; Pancreatic cancer;Machine learning

        人工智能(Artificial intelligence,AI)是一門新的技術(shù)科學(xué),主要研究開發(fā)能夠模擬、延伸和擴(kuò)展人類智能的理論、方法、技術(shù)和應(yīng)用系統(tǒng)。21世紀(jì)人工智能得到了飛速發(fā)展,在醫(yī)療、軍事、化學(xué)工業(yè)、地質(zhì)勘探等各個(gè)領(lǐng)域都取得了驚人的成果。早在20世紀(jì)50年代后期,人工智能就在醫(yī)療領(lǐng)域有了研究,在醫(yī)學(xué)診斷中有了初步的探索[1]。70余年來(lái),在經(jīng)歷了曲折的發(fā)展之后,目前人工智能在我國(guó)臨床診斷、治療,病原學(xué)檢測(cè),疾病預(yù)后預(yù)測(cè)及醫(yī)療影像等方面應(yīng)用廣泛,為我國(guó)的醫(yī)療事業(yè)做出巨大貢獻(xiàn)[2]。其中在胰腺疾病的診斷及治療方面,已經(jīng)有多種人工智能技術(shù)在應(yīng)用,如胰腺炎的診斷及預(yù)測(cè)、胰腺惡性腫瘤的診斷及鑒別診斷、人工胰島的應(yīng)用等。本文就人工智能在胰腺疾病診斷及治療中的應(yīng)用及展望作出綜述?,F(xiàn)報(bào)道如下。

        1 人工智能在醫(yī)學(xué)研究中的主要方法

        1956年,在由一些心理學(xué)、神經(jīng)生理學(xué)、計(jì)算機(jī)學(xué)等學(xué)科參加的達(dá)特茅斯會(huì)議中,“人工智能”的概念首次被提出,并希望可以用計(jì)算機(jī)來(lái)構(gòu)造擁有與人類智慧同樣本質(zhì)特性的機(jī)器。人工智能研究領(lǐng)域范圍很廣,包括專家系統(tǒng)、機(jī)器學(xué)習(xí)、進(jìn)化計(jì)算、模糊邏輯、計(jì)算機(jī)視覺(jué)、自然語(yǔ)言處理、推薦系統(tǒng)等。其中機(jī)器學(xué)習(xí)與醫(yī)學(xué)研究關(guān)系最為密切。機(jī)器學(xué)習(xí)(Machine learning,ML)其實(shí)是一種實(shí)現(xiàn)人工智能的方法[3]。簡(jiǎn)而言之就是使用算法來(lái)解析已有的臨床數(shù)據(jù),從中學(xué)習(xí),然后對(duì)臨床事件做出決策和預(yù)測(cè)。與傳統(tǒng)的為解決特定任務(wù)、硬編碼的軟件程序不同,機(jī)器學(xué)習(xí)是用大量的數(shù)據(jù)來(lái)“訓(xùn)練”,通過(guò)各種算法從數(shù)據(jù)中學(xué)習(xí)如何完成任務(wù)。傳統(tǒng)的機(jī)器學(xué)習(xí)算法包括決策樹、聚類、貝葉斯分類、支持向量機(jī)、EM、Adaboost等。從學(xué)習(xí)方法上來(lái)分,機(jī)器學(xué)習(xí)算法可以分為監(jiān)督學(xué)習(xí)(如分類問(wèn)題)、無(wú)監(jiān)督學(xué)習(xí)(如聚類問(wèn)題)、半監(jiān)督學(xué)習(xí)、集成學(xué)習(xí)、深度學(xué)習(xí)和強(qiáng)化學(xué)習(xí)等。目前,深度學(xué)習(xí)(Deep learning,DL)[4]方法在醫(yī)學(xué)研究中應(yīng)用最為廣泛。由于醫(yī)療數(shù)據(jù)具有龐大、復(fù)雜、無(wú)序的特殊性,傳統(tǒng)的機(jī)器學(xué)習(xí)方法并不能勝任這樣繁雜的任務(wù)。而深度學(xué)習(xí)采用了深度神經(jīng)網(wǎng)絡(luò)(DNN)、卷積神經(jīng)網(wǎng)絡(luò)(CNN)等方法,與傳統(tǒng)計(jì)算機(jī)回歸分析的單層結(jié)構(gòu)不同,神經(jīng)網(wǎng)絡(luò)是復(fù)雜的多層感知模型,包括了輸入層、模擬神經(jīng)元層、輸出層三個(gè)部分。其在數(shù)據(jù)處理能力上可以分析傳統(tǒng)的回歸分析所無(wú)法處理的非線性數(shù)據(jù)。只要選擇合適的輸入層與輸出層,通過(guò)網(wǎng)絡(luò)模型對(duì)大量臨床數(shù)據(jù)的學(xué)習(xí)和調(diào)試,就能找到一個(gè)輸入層與輸出層的函數(shù)關(guān)系,一個(gè)無(wú)限靠近現(xiàn)實(shí)真相的關(guān)聯(lián)關(guān)系。使用訓(xùn)練成功的網(wǎng)絡(luò)模型,對(duì)臨床工作具有巨大的推動(dòng)作用。

        2 人工智能在胰腺疾病診斷及治療中的應(yīng)用

        2.1人工智能與胰腺炎

        急性胰腺炎是一種常見的急腹癥,其發(fā)病率與死亡率均較高[5-6]。自從人工智能發(fā)展以來(lái),其在急性胰腺炎預(yù)測(cè)方面就有了不少研究與探索。上世紀(jì)九十年代,Pofahl WE等[7]對(duì)神經(jīng)網(wǎng)絡(luò)在預(yù)測(cè)急性胰腺炎患者住院時(shí)間(Length of stay,LOS)中的作用展開了研究。他們建立了一種反向傳播神經(jīng)網(wǎng)絡(luò),并對(duì)195例急性胰腺炎患者的病例資料進(jìn)行回顧,其中156例用于對(duì)神經(jīng)網(wǎng)絡(luò)模型的訓(xùn)練,在剩余39例中進(jìn)行測(cè)試。結(jié)果表明,相比于其他方法,神經(jīng)網(wǎng)絡(luò)模型在預(yù)測(cè)LOS>7 d中具有最高的靈敏度(75%)。盡管該研究并未涉及急性胰腺炎發(fā)病早期的預(yù)測(cè),但也證實(shí)了人工智能在急性胰腺炎領(lǐng)域擁有廣闊的研究前景。之后Keogan MT[8]團(tuán)隊(duì)也利用人工智能對(duì)急性胰腺炎患者的預(yù)后進(jìn)行預(yù)測(cè)。他們建立的人工神經(jīng)網(wǎng)絡(luò)模型(ANN)利用CT和實(shí)驗(yàn)室數(shù)據(jù)對(duì)92例急性胰腺炎患者的預(yù)后進(jìn)行預(yù)測(cè)。輸入節(jié)點(diǎn)為CT、實(shí)驗(yàn)室數(shù)據(jù),輸出節(jié)點(diǎn)為患者住院時(shí)間。最后ANN成功地預(yù)測(cè)了患者有無(wú)超過(guò)平均住院時(shí)間(Az=0.83±0.05)。相比于Ranson分級(jí)(Az=0.68±0.06,P<0.02)和Balthazar分級(jí)(Az=0.62±0.06,P<0.003),他們建立的人工神經(jīng)網(wǎng)絡(luò)模型有著明顯優(yōu)勢(shì)。但與線性判別分析(Az=0.82±0.05,P=0.53)相比,其結(jié)果不具有差異。此外,對(duì)于急性胰腺炎嚴(yán)重程度的預(yù)測(cè),有研究者建立了一個(gè)神經(jīng)網(wǎng)絡(luò)預(yù)后模型[9]。該模型經(jīng)增強(qiáng)CT掃描證實(shí)其敏感性為100%,入院時(shí)特異性為70%。Pearce CB等[10]利用機(jī)器學(xué)習(xí)來(lái)提高APACHEⅡ評(píng)分和C反應(yīng)蛋白的入院值對(duì)急性胰腺炎嚴(yán)重程度的預(yù)測(cè)作用。選取了256例患者作為研究對(duì)象,采用年齡、CRP、呼吸頻率、空氣中PO2、動(dòng)脈pH、血肌酐、白細(xì)胞計(jì)數(shù)和GCS評(píng)分這8個(gè)項(xiàng)目作為輸入節(jié)點(diǎn),其受試者-操作特征曲線(AUC)下的面積為0.82(SD 0.01),預(yù)測(cè)嚴(yán)重程度的最佳臨界值為0.87,特異度為0.71。預(yù)測(cè)結(jié)果明顯優(yōu)于入院APACHE Ⅱ評(píng)分(AUC 0.74)和歷史入院APACHE Ⅱ數(shù)據(jù)(AUC 0.68~0.75)(P=0.0036)。表明機(jī)器學(xué)習(xí)技術(shù)顯著改善了入院后首次觀察值的預(yù)測(cè)性能,并減少了預(yù)測(cè)因素的數(shù)量。另一項(xiàng)研究[11]將神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)急性胰腺炎嚴(yán)重程度的準(zhǔn)確性與APACHE Ⅱ和GCS評(píng)分進(jìn)行比較,結(jié)果顯示ANN在預(yù)測(cè)嚴(yán)重病程進(jìn)展(P<0.05和P< 0.01)、預(yù)測(cè)多器官功能障礙綜合征的發(fā)展(P<0.05和P<0.01)以及預(yù)測(cè)AP死亡(P<0.05)方面優(yōu)于APACHE Ⅱ或GS評(píng)分系統(tǒng),其靈敏度和特異度分別達(dá)到89%、96%。急性胰腺炎是一種很復(fù)雜的疾病,根據(jù)之前的研究可以得出,想要利用人工智能預(yù)測(cè)急性胰腺炎的嚴(yán)重程度,危險(xiǎn)因素的選擇十分關(guān)鍵[12]。Andersson B等[13]設(shè)計(jì)的人工神經(jīng)網(wǎng)絡(luò)模型,首次將疼痛持續(xù)時(shí)間作為危險(xiǎn)變量提出。然而,Hong WD等[14]指出該研究的幾個(gè)局限性:樣本量小,缺少數(shù)據(jù)點(diǎn),急性胰腺炎發(fā)病和數(shù)據(jù)收集之間的時(shí)間間隔不清楚,所以該研究結(jié)果有待進(jìn)一步闡明。急性胰腺炎癥狀出現(xiàn)后第一周內(nèi)持續(xù)的器官衰竭一個(gè)致命結(jié)局的標(biāo)志,Hong WD等[14]認(rèn)為,這可以作為使用人工神經(jīng)網(wǎng)絡(luò)分析急性胰腺炎患者持續(xù)性器官衰竭的預(yù)測(cè)因素之一。同時(shí),他們也提到了該研究的一些局限性,如數(shù)據(jù)是回顧性的,樣本量較小,可能會(huì)造成結(jié)果的一些偏差。另有一篇綜述[15]表示,與當(dāng)前的評(píng)分系統(tǒng)相比,神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)疾病嚴(yán)重程度的準(zhǔn)確性更高,需要的變量更少,并且能更早地作出評(píng)估。但是van den Heever M等[15]也發(fā)現(xiàn),現(xiàn)有的大部分研究,其數(shù)據(jù)來(lái)源的數(shù)據(jù)庫(kù)大多是為管理目的而設(shè)計(jì)的,對(duì)臨床或研究人員價(jià)值有限,希望未來(lái)能建立智能數(shù)據(jù)庫(kù),促進(jìn)多中心數(shù)據(jù)收集。

        急性胰腺炎本身病程十分復(fù)雜,在疾病發(fā)展過(guò)程中會(huì)出現(xiàn)各種各樣的并發(fā)癥[16]。Fei Y等[17]的一項(xiàng)研究利用人工神經(jīng)網(wǎng)絡(luò)模型來(lái)預(yù)測(cè)門脾靜脈血栓形成的能力,并與傳統(tǒng)Logistic回歸進(jìn)行比較。結(jié)果顯示所建立的人工神經(jīng)網(wǎng)絡(luò)模型靈敏度為80%,特異度為85.7%,陽(yáng)性預(yù)測(cè)值為77.6%,陰性預(yù)測(cè)值為90.7%。準(zhǔn)確率為83.3%。綜合性能優(yōu)于Logistic回歸模型。如果能加入更多的臨床因素或生物標(biāo)志,該模型的預(yù)測(cè)能力也許會(huì)進(jìn)一步提高。于是Fei Y等[18]改進(jìn)了研究方法,采用徑向基函數(shù)(RBF)人工神經(jīng)網(wǎng)絡(luò)(ANN)模型預(yù)測(cè)AP誘發(fā)PVT的風(fēng)險(xiǎn),結(jié)果得出RBF神經(jīng)網(wǎng)絡(luò)模型預(yù)測(cè)PVT的敏感性、特異性和準(zhǔn)確性分別為76.2%、92.0%和88.1%。該研究證明RBF神經(jīng)網(wǎng)絡(luò)模型是預(yù)測(cè)AP后PVT風(fēng)險(xiǎn)的有效工具,并且提出AMY、D-二聚體、PT和HCT是AP誘發(fā)PVT的重要預(yù)測(cè)因子。以同樣的方法,F(xiàn)ei Y等[19]人對(duì)重癥急性胰腺炎(SAP)并發(fā)急性肺損傷(ALI)的危險(xiǎn)性也做了相關(guān)探索,并得到陽(yáng)性結(jié)果。最近還有一項(xiàng)研究[20]表明基于CECT的放射組學(xué)模型在預(yù)測(cè)AP復(fù)發(fā)方面效果良好。這可能為一些復(fù)發(fā)患者就預(yù)防措施方面提供重要幫助。

        慢性胰腺炎是各種病因引起胰腺組織和功能不可逆改變的慢性炎癥性疾病,終末期有嚴(yán)重的并發(fā)癥,包括內(nèi)外分泌功能不全和胰管腺癌。慢性胰腺炎是胰管腺癌的危險(xiǎn)因素之一[21],人工智能在慢性胰腺炎領(lǐng)域尚未做深入研究,現(xiàn)有研究主要利用人工智能相關(guān)算法鑒別診斷胰腺癌與慢性胰腺炎[22-25]。目前主要采用的方法是利用實(shí)時(shí)內(nèi)鏡超聲(EUS)彈性成像提供關(guān)于胰腺病變特征的附加信息,再通過(guò)人工神經(jīng)網(wǎng)絡(luò)分析,最后使用計(jì)算機(jī)輔助診斷來(lái)評(píng)估實(shí)時(shí)EUS彈性成像在胰腺局灶性病變中的準(zhǔn)確性。其中一項(xiàng)研究[26]中神經(jīng)網(wǎng)絡(luò)計(jì)算方法的敏感性為87.59%,特異性為82.94%,陽(yáng)性預(yù)測(cè)值為96.25%,陰性預(yù)測(cè)值為57.22%,說(shuō)明使用人工智能方法可以提供快速準(zhǔn)確的診斷。自身免疫性胰腺炎(AIP)是慢性胰腺炎的一個(gè)獨(dú)特亞型,其臨床表現(xiàn)與胰腺導(dǎo)管腺癌(PDA)有許多相似之處。Zhang Y等[27]利用多種特征提取算法對(duì)CT和PET圖像進(jìn)行紋理特征提取,結(jié)果顯示病灶紋理分析有助于準(zhǔn)確區(qū)分AIP和PDA。

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        (收稿日期:2020-03-03)

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