The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cancers of the cervix and thyroid using AI. Conclusion: The use of the CAD method can increase the quality of life by avoiding intra and postoperative complications in surgery, intraoperative orientation and the precise determination of radiation doses and irradiation zone in radiotherapy.

Verifying the Effects of an Education Program Leveraging Information Technology to Promote Cervical Cancer Screening in Women Aged 20 - 29—A One-Year Longitudinal Study

The purpose of this research is to implement an IT-based education program in order to promote cervical cancer screenings for women aged 20 - 29 years, as well as to examine the results of said program. This is a longitudinal/comparative study of two groups, one for which the program was implemented (the intervention group), and the other for which it was not (the control group). The program consisted of attending a health lecture and encouragement to be screened one month, six months, and one year later sent through IT-based methods. The target was unmarried women aged 20 - 29 who had neither previously given birth nor had been screened for cervical cancer in a period one year prior. They were divided into two groups, the intervention group (n = 142) and control group (n = 145). The effectiveness of the program was assessed via an initial survey and further surveys six months and one year later. Results were based on the Japanese version of the Health Belief Model Scale for Cervical Cancer and the Pap Smear Test (HBMSCCPST), knowledge scores in the categories of Healthy Lifestyles, Cervical Cancer, Cervical Cancer Screening, and screening behavior. A two-way ANOVA of the HBMSCCPST subscales and knowledge scores in the initial, six-month, and one-year surveys was performed, showing interaction in Cervical Cancer (p = 0.00). Main effects were observed in Cervical Cancer Screening (p = 0.00) and Healthy Lifestyles (p = 0.00). Regarding the amount of change from the initial survey, knowledge scores in the Cervical Cancer (p = 0.027) and Cervical Cancer Screening (p = 0.016) categories were significantly higher in the intervention group than in the control group. There was no significant difference in cervical cancer screening rates (p = 0.26) between the two groups. However, a small-degree effect size was observed for Benefits, Seriousness, and Susceptibility subscales in both examinees and non-examinees. Although the educational program of this study was effective in improving the knowledge of women in their twenties, there was little improvement in HBMSCCPST and it did not lead to the promotion of cervical cancer screening. In order to raise interest in cervical cancer screening, it is necessary to consider useful content to guide women to consult with healthcare professionals, a long-term population approach, and organizational structure of consultation.

Colorectal cancer screening:A review of current knowledge and progress in research

Colorectal cancer(CRC)is one of the most prevalent malignancies worldwide,being the third most commonly diagnosed malignancy and the second leading cause of cancer-related deaths globally.Despite the progress in screening,early diagnosis,and treatment,approximately 20%-25%of CRC patients still present with metastatic disease at the time of their initial diagnosis.Furthermore,the burden of disease is still expected to increase,especially in individuals younger than 50 years old,among whom early-onset CRC incidence has been increasing.Screening and early detection are pivotal to improve CRC-related outcomes.It is well established that CRC screening not only reduces incidence,but also decreases deaths from CRC.Diverse screening strategies have proven effective in decreasing both CRC incidence and mortality,though variations in efficacy have been reported across the literature.However,uncertainties persist regarding the optimal screening method,age intervals and periodicity.Moreover,adherence to CRC screening remains globally low.In recent years,emerging technologies,notably artificial intelligence,and non-invasive biomarkers,have been developed to overcome these barriers.However,controversy exists over the actual impact of some of the new discoveries on CRC-related outcomes and how to effectively integrate them into daily practice.In this review,we aim to cover the current evidence surrounding CRC screening.We will further critically assess novel approaches under investigation,in an effort to differentiate promising inno-vations from mere novelties.

A study on service capacity of primary medical and health institutions for cervical cancer screening in urban and rural areas in China

Objective: To provide a decision-making basis for sustainable and effective development of cervical cancer screening.Methods: This cross-sectional study assesses the service capacity to conduct cervical cancer screening with a sample of 310 medical staff, medical institutions and affiliated township health centers from 20 countylevel/district-level areas in 14 Chinese provinces in 2016.Results: The county-level/district-level institutions were the main prescreening institutions for cervical cancer screening. More medical staff have become engaged in screening, with a significantly higher amounts in urban than in rural areas(P<0.05). The number of human papillomavirus(HPV) testers grew the fastest(by 225% in urban and 125% in rural areas) over the course of the project. HPV testing took less time than cytology to complete the same number of screening tasks in both urban and rural areas. The proportion of mid-level professionals was the highest among the medical staff, 40.0% in urban and 44.7% in rural areas(P=0.406), and most medical staff had a Bachelor’s degree, accounting for 76.3% in urban and 52.0% in rural areas(P<0.001). In urban areas, 75.0% were qualified medical staff, compared with 68.0% in rural areas, among which the lowest proportion was observed for rural cytology inspectors(22.7%). The medical equipment for cervical pathology diagnosis in urban areas was better(P<0.001). HPV testing equipment was relatively adequate(typing test equipment was 70% in urban areas, and non-typing testing equipment was 70% in rural areas).Conclusions: The service capacity of cervical cancer screening is insufficient for the health needs of the Chinese population. HPV testing might be an optimal choice to fill the needs of cervical cancer screening given current Chinese medical health service capacity.

Artificial intelligence for renal cancer:From imaging to histology and beyond

Artificial intelligence(AI)has made considerable progress within the last decade and is the subject of contemporary literature.This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation.Renal cell carcinoma(RCC)has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging.This creates considerable challenges as approximately 10%e17%of kidney tumors are designated as benign in histopathological evaluation;however,certain co-morbid populations(the obese and elderly)have an increased peri-interventional risk.AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions.The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC.Currently,AI applications can be found in any aspect of RCC management including diagnostics,perioperative care,pathology,and follow-up.Most commonly applied models include neural networks,random forest,support vector machines,and regression.However,for implementation in daily practice,health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets,define meaningful endpoints,and unify interpretation.

Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma

Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades.Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset,contributing to its late presentation.In the recent years,artificial intelligence(AI)as emerged as a field to aid in the process of clinical decision making.Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma.This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma,and factors which may limit its use in a clinical setting.

Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Several studies have shown a significant adenoma miss rate up to 35%during screening colonoscopy,especially in patients with diminutive adenomas.The use of artificial intelligence(AI)in colonoscopy has been gaining popularity by helping endoscopists in polyp detection,with the aim to increase their adenoma detection rate(ADR)and polyp detection rate(PDR)in order to reduce the incidence of interval cancers.The efficacy of deep convolutional neural network(DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos.Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR.In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.

Early diagnosis of pancreatic cancer:What strategies to avoid a foretold catastrophe

While great strides in improving survival rates have been made for most cancers in recent years,pancreatic ductal adenocarcinoma(PDAC)remains one of the solid tumors with the worst prognosis.PDAC mortality often overlaps with incidence.Surgical resection is the only potentially curative treatment,but it can be performed in a very limited number of cases.In order to improve the prognosis of PDAC,there are ideally two possible ways:the discovery of new strategies or drugs that will make it possible to treat the tumor more successfully or an earlier diagnosis that will allow patients to be operated on at a less advanced stage.The aim of this review was to summarize all the possible strategies available today for the early diagnosis of PDAC and the paths that research needs to take to make this goal ever closer.All the most recent studies on risk factors and screening modalities,new laboratory tests including liquid biopsy,new imaging methods and possible applications of artificial intelligence and machine learning were reviewed and commented on.Unfortunately,in 2022 the results for this type of cancer still remain discouraging,while a catastrophic increase in cases is expected in the coming years.The article was also written with the aim of highlighting the urgency of devoting more attention and resources to this pathology in order to reach a solution that seems more and more unreachable every day.

Established and Emerging Optical Technologies for the Real-Time Detection of Cervical Neoplasia: A Review

Cervical cancer remains a critically important problem for women, especially those women in the developing world where the case-fatality rate is high. There are an estimated 528,000 cases and 266,000 deaths worldwide. Established screening and detection programs in the developed world have lowered the mortality from 40/100,000 to 2/100,000 over the last 60 years. The standard of care has been and continues to be: a screening Papanicolaou smear with or without Human Papilloma Virus (HPV) testing;followed by colposcopy and biopsies and if the smear is abnormal;and followed by treatment if the biopsies show high grade disease (cervical intraepithelial neoplasia (CIN) grades 2 and 3 and Carcinoma-in-situ). Low grade lesions (Pap smears with Atypical Cells of Uncertain Significance (ASCUS), Low Grade Squamous Intraepithelial Lesions (LGSIL), biopsies showing HPV changes or showing CIN 1);are usually followed for two years and then treated if persistent. Treatment can be performed with loop excision, LASER, or cryotherapy. Loop excision yields a specimen which can be reviewed to establish the diagnosis more accurately. LASER vaporizes the lesion and cryotherapy leads to tissue destruction. Under long term study;loop excision, LASER, and cryotherapy have the same rate of cure. The standard of care is expensive and takes 6 - 12 weeks for the individual patient. During the last twenty years, new technologies that can view the cervix and even image the cervix with cellular resolution have been developed. These technologies could lead to a new paradigm in which diagnosis and treatment occurs at a single visit. These technologies include fluorescence and reflectance spectroscopy (probe or wide-field, whole cervix scanning approaches) and fluorescence confocal endomicroscopy or high resolution micro-endoscopy. Both technologies have received Federal Drug Administration (FDA) and have been commercialized. Research trials continue to show their remarkable performance. These technologies are reviewed and clinical trials are summarized. Emerging technologies are coming along that may compete with those already approved and include optical coherence tomography, optical coherence tomography with autofluorescence, diffuse optical microscopy, and dual mode micro-endoscopy. These technologies are also reviewed and where available, clinical data is reported. Optical technologies are ready to diffuse into clinical practice because they will save money and 3 or 4 visits in the developed world and offer the same standard of care to the developing world where more cervical cancer exists.

《人工智能辅助宫颈细胞学诊断技术的应用及质量控制专家共识》解读

2023年12月中国妇幼保健协会和中国妇幼保健协会妇女病防治专业委员会共同发布了《人工智能辅助宫颈细胞学诊断技术的应用及质量控制专家共识》(以下简称《共识》)。本文旨在对《共识》进行解读,便于相关专业人员在工作中进一步理解和实践。

在老年女性中以人工智能+云诊断细胞学阅片为主导的宫颈癌联合筛查模式应用分析

目的探讨在老年女性中基于人工智能+云诊断细胞学阅片为主导的联合宫颈癌筛查模式的应用效果及价值。方法回顾性分析2017年1月至2021年12月连云港市妇幼保健院行宫颈锥切治疗的319例宫颈上皮内瘤变(CIN)患者病历资料,其中传统细胞学检查联合人乳头瘤病毒(HPV)检测84例作为A组,人工智能+云诊断细胞学阅片联合HPV检测127例作为B组,人工智能+云诊断细胞学阅片联合高危HPV E6/E7 mRNA检测108例作为C组,比较各组年龄、孕次、阴道镜活检点数、绝经率、转化区完全可见率、宫颈管搔刮率、治疗间隔时间、冷刀锥切率、锥切后病变一致率、锥切后病变升级率。结果各组之间年龄、孕次、阴道镜活检点数、绝经率、转化区完全可见率、宫颈管搔刮率、治疗间隔时间、冷刀锥切率等指标比较,差异无统计学意义(P>0.05)。B组和C组锥切后病理结果的一致率高于A组,病变升级率低于A组,差异有统计学意义(P<0.05)。结论在老年女性中,基于人工智能+云诊断细胞学阅片技术的宫颈癌联合筛查模式具有更低的漏诊率和更高的准确率,更适用于老年女性宫颈癌筛查。

人工智能辅助系统在宫颈薄层液基细胞学研究中的应用

目的探讨人工智能(AI)辅助系统在宫颈薄层液基细胞学研究中的应用。方法收集2022年7月至2023年2月湖州市妇幼保健院的1994例宫颈薄层液基细胞学涂片样本。以阴道镜活检结果宫颈上皮内病变(CIN)1级及以上(≥CIN 1级)为金标准,分别采用人工阅片、AI阅片和AI辅助系统阅片(AI辅助阅片)3种方式对宫颈细胞学液基薄层涂片进行阅片。比较不同宫颈薄层液基细胞学检测(TCT)结果下不同年龄段样本3种阅片方式阳性率、灵敏度和特异度以及3种阅片方式的阅片时长。结果1994例样本中,活检结果≥CIN 1级共102例,总体阳性率为5.12%,TCT结果为意义不明的非典型鳞状细胞(ASCUS)及以上(≥ASC-US)的阳性率为9.63%,低度鳞状上皮内病变(LSIL)及以上(≥LSIL)的阳性率为4.91%。TCT结果≥ASC-US时,≥30岁组样本AI阅片阳性率高于18~29岁组,差异有统计学意义(P<0.05);而18~29岁和30岁组人工阅片和AI辅助阅片阳性率比较差异均无统计学意义(均P>0.05)。TCT结果≥LSIL时,18~29岁和30岁组人工阅片、AI阅片和AI辅助阅片阳性率比较差异均无统计学意义(均P>0.05)。TCT结果≥ASC-US时,人工阅片的灵敏度及特异度分别为100.00%、95.30%,AI辅助阅片的灵敏度及特异度分别为95.10%、94.66%,两种阅片方式的灵敏度、特异度比较差异均无统计学意义(均P>0.05)。TCT结果≥LSIL时,人工阅片的灵敏度及特异度分别为66.67%、98.78%,AI辅助阅片的灵敏度及特异度分别为82.35%、97.15%,AI辅助阅片的灵敏度高于人工阅片,差异有统计学意义(P<0.05),而特异度比较差异无统计学意义(P>0.05)。与人工阅片比较,AI辅助阅片的总阅片时长减少25.34 h,总阅片时长减少率为50.76%(25.34/49.92);平均每张涂片减少45.75 s,平均阅片时长减少率为50.77%(45.75/90.12)。结论病理医师运用AI辅助系统进行宫颈癌变筛查时,可以保证阅片准确度,提高细胞学阅片效率。

人工智能在前列腺癌中的应用研究进展

前列腺癌(prostate cancer,PCa)已成为我国男性泌尿生殖系统最高发的恶性肿瘤,临床上受到了广泛关注。随着技术的进步和多学科合作的深入,人工智能(artificial intelligence,AI)逐渐在医疗领域展现出独有的优势和应用价值。为帮助临床医生更好地了解相关AI技术,更精准高效地选择合适的AI算法模型,本文简要介绍了PCa领域常用的AI技术,综述近年来其在PCa的诊断、治疗、预后判断及早期筛查中的应用。

MRI功能成像及定量成像技术在宫颈癌诊疗中的应用述评

宫颈癌(cervical cancer,CC)是我国女性癌症中第五大常见癌症,且发病有年轻化的趋势,严重威胁女性的生命健康。不同分期及风险者治疗方案不尽相同,随着保育手术普及,对术前精准分期和风险评估提出了更高的要求。MRI是CC诊断、分期和疗效评估的重要方法。但MRI常规序列对CC的诊断及评估受限于主观经验,且缺乏客观定量,准确性欠佳。MRI功能成像及定量成像等新技术,能提供血流动力学改变、组织微观结构的变化、肿瘤乏氧环境以及细胞增殖和蛋白代谢等多维度的精准定量信息,用于CC术前精准诊断和风险评估,为全面了解肿瘤的病理生理、代谢等提供可视化依据。借助人工智能技术挖掘影像大数据,有助于解决临床难题。本文将针对CC分期、疗效及是否复发评估等临床难题,综述MRI功能成像及定量成像在CC诊疗中的应用进展,以推动其临床应用,提升诊疗水平。

全基因测序技术在宫颈癌诊断中的应用效果

目的探讨全基因测序技术在宫颈癌诊断中的应用效果。方法将200例宫颈病变患者分为模型组62例和验证组138例。所有患者均接受宫颈细胞的全基因组序列分析,基于染色体拷贝数变异的分析结果构建C-Score模型,使用验证组的数据对构建的C-Score模型进行验证,评估其在实际应用中的有效性和准确性。结果宫颈癌患者宫颈脱落细胞及肿瘤组织的染色体测序结果显示,染色体拷贝数不断变化,且肿瘤细胞与宫颈脱落细胞染色体拷贝数的变异表现出一致性。通过对模型组宫颈鳞状细胞癌样本的检测发现,第3条、第5条、第11条染色体上存在最多的拷贝数变异,根据该拷贝数变异,可以成功建立C-Score模型。验证组构建的C-Score模型诊断宫颈癌的灵敏度为94.1%,特异度为96.8%,曲线下面积为0.94。所有患者构建的C-Score模型诊断宫颈癌的灵敏度提高到了97.2%,特异度提高到了97.4%,曲线下面积提高到了0.98。结论基于全基因测序技术的宫颈癌诊断方法具有较高的准确性和可行性,可以为临床提供更有效的诊断手段。

Artificial intelligence empowers the second-observer strategy for colonoscopy:a randomized clinical trial

Background:In colonoscopy screening for colorectal cancer,human vision limitationsmay lead to highermiss rate of lesions;artificial intelligence(AI)assistance has been demonstrated to improve polyp detection.However,there still lacks direct evidence to demonstrate whether AI is superior to trainees or experienced nurses as a second observer to increase adenoma detection during colonoscopy.In this study,we aimed to compare the effectiveness of assistance fromAI and human observer during colonoscopy.Methods:A prospective multicenter randomized study was conducted from 2 September 2019 to 29 May 2020 at four endoscopy centers in China.Eligible patients were randomized to either computer-aided detection(CADe)-assisted group or observer-assisted group.The primary outcome was adenoma per colonoscopy(APC).Secondary outcomes included polyp per colonoscopy(PPC),adenoma detection rate(ADR),and polyp detection rate(PDR).We compared continuous variables and categorical variables by using R studio(version 3.4.4).Results:A total of 1,261(636 in the CADe-assisted group and 625 in the observer-assisted group)eligible patients were analysed.APC(0.42 vs 0.35,P=0.034),PPC(1.13 vs 0.81,P<0.001),PDR(47.5%vs 37.4%,P<0.001),ADR(25.8%vs 24.0%,P=0.464),the number of detected sessile polyps(683 vs 464,P<0.001),and sessile adenomas(244 vs 182,P=0.005)were significantly higher in the CADe-assisted group than in the observer-assisted group.False detections of the CADe system were lower than those of the human observer(122 vs 191,P<0.001).Conclusions:Compared with the human observer,the CADe system may improve the clinical outcome of colonoscopy and reduce disturbance to routine practice(Chictr.org.cn No.:ChiCTR1900025235).

人工智能在乳腺癌影像筛查领域的应用进展

乳腺癌是女性常见的恶性肿瘤之一,严重威胁着女性的身心健康。早发现、早诊断、早治疗是改善乳腺癌预后的关键。因此,基于影像学检查的乳腺癌筛查尤为重要。近年来,人工智能(AI)在医学图像分析领域的研究受到前所未有的热情,显示出巨大潜能。因此,本文将对AI在乳腺癌影像筛查工作中的应用研究及面临的挑战进行综述。

联合C-TIRADS指南的超声辅助诊断模型对甲状腺癌筛查的应用价值

目的探讨联合甲状腺结节超声恶性危险分层中国指南(Chinese-Thyroid Imaging Reporting and Data System,C-TIRADS)构建的超声辅助诊断模型对甲状腺结节良恶性筛查的应用价值。方法回顾性分析2022年4月—2023年4月在江门市五邑中医院进行超声检查并明确病理结果的甲状腺结节患者(共136例患者,180个病灶),依据C-TIRADS指南对甲状腺结节进行分类评估,然后使用AI辅助诊断联合C-TIRADS再次进行分类评估,以病理结果为金标准,绘制C-TIRADS诊断与AI联合C-TIRADS诊断的ROC曲线,比较两种诊断方法的AUC及敏感度、特异度、准确度等指标,分析两组指标差异。绘制校准曲线和DCA曲线进行验证对比,评价其校准能力和临床效用。结果180个甲状腺结节病灶经手术病理证实良性87个,恶性93个。C-TIRADS诊断与AI联合C-TIRADS诊断对甲状腺结节良恶性诊断的AUC分别为0.714、0.800,AI联合C-TIRADS诊断明显高于C-TIRADS诊断,差异有统计学意义(P<0.001)。两种诊断方法均有良好的校准能力和临床效用,AI联合C-TIRADS诊断较C-TIRADS诊断更优。结论联合C-TIRADS的AI辅助诊断模型在甲状腺结节良恶性的诊断中具有良好的诊断效能、校准能力及临床效用,能有效减少甲状腺结节的过度诊疗,对临床决策有一定参考意义。

人工智能电子阴道镜辅助诊断系统在宫颈癌筛查中的价值

目的分析人工智能(AI)电子阴道镜辅助诊断系统(简称AI电子阴道镜)在宫颈癌筛查中的价值。方法收集2023年1月至12月在广西壮族自治区妇幼保健院宫颈疾病诊治中心行阴道镜检查的214例患者,以病理结果为金标准,统计传统阴道镜、AI电子阴道镜的阳性检出率,并分析两者检查结果与病理结果的一致性。结果病理结果显示,宫颈上皮内瘤变(CIN)111例,CIN243例,CIN3115例,宫颈癌45例。AI电子阴道镜的阳性检出率为94.86%(203/214),与病理结果有较好的一致性(Kappa=0.851,P<0.001);传统阴道镜的阳性检出率为83.64%(179/214),与病理结果的一致性一般(Kappa=0.561,P=0.010)。结论AI电子阴道镜可以提高阳性检出率,与病理结果的一致性较高,有较好的临床应用前景,值得推广。

Artificial intelligence in theranostics of gastric cancer, a review

Gastric cancer (GC) is one of the commonestcancers with high morbidity and mortality in the world.How to realize precise diagnosis and therapy of GC ownsgreat clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to earlydiagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in earlyscreening, diagnosis, therapy and prognosis of stomachcarcinoma. Especially AI combined with breath screeningearly GC system improved 97.4 % of early GC diagnosisratio, AI model on stomach cancer diagnosis system of salivabiomarkers obtained an overall accuracy of 97.18 %, speci-ficity of 97.44 %, and sensitivity of 96.88 %. We also discussconcept, issues, approaches and challenges of AI applied instomach cancer. This review provides a comprehensive viewand roadmap for readers working in this field, with the aimof pushing application of AI in theranostics of stomachcancer to increase the early discovery ratio and curativeratio of GC patients.