Published online Aug 8, 2026. doi: 10.35712/aig.123751
Revised: June 12, 2026
Accepted: June 30, 2026
Published online: August 8, 2026
Processing time: 71 Days and 1.6 Hours
Abdominal tuberculosis (ATB) remains a major diagnostic challenge in high-burden low-income and middle-income countries due to nonspecific symptoms, paucibacillary disease, low microbiological confirmation rates, and mimicry of Crohn’s disease and other conditions. Artificial intelligence (AI) and machine learning have shown promise in pulmonary tuberculosis but their role in ATB diagnosis is still emerging.
To map the existing evidence on AI applications for ATB diagnosis, and propose a translational roadmap for equitable clinical integration.
This Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews-compliant scoping review searched PubMed, Cochrane Library, and Google Scholar up to April 2026. Two inde
Seventeen studies (16 original retrospective studies and one systematic review) were included. Fourteen studies focused on differentiating intestinal tuberculosis from Crohn’s disease using radiomics, endoscopic deep learning, histopathology, and multimodal approaches, with area under the curves ranging from 0.80-0.96. Multimodal and explainable models performed best. Major gaps included the absence of management-focused applications, pre
AI and machine learning demonstrate strong potential to improve abdominal tuberculosis differentiation from mimics; however, prospective validation in diverse low-income and middle-income countries populations and in
Core Tip: This is the first dedicated Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews-compliant scoping review of artificial intelligence and machine learning for abdominal tuberculosis diagnosis and differential diagnosis. It demonstrates high accuracy of radiomics, endoscopic convolutional neural networks, and multimodal models in differentiating abdominal tuberculosis from Crohn’s disease and other mimics, yet reveals a complete absence of management-focused artificial intelligence applications, and predominance of composite reference standards. The proposed four-phase roadmap provides a practical, equity-centered pathway from retrospective evidence to real-world deployment, prioritizing microbiological reference standards, bias audits, infrastructure-appropriate tools, and explicit integration with existing hierarchical diagnostic criteria to reduce diagnostic delays and unnecessary interventions in high-burden settings.
- Citation: Kaushik K, Pathania J, Singh PK, Dinkar M, Pathania V. Artificial intelligence and machine learning applications in abdominal tuberculosis diagnosis: A scoping review and translational roadmap. Artif Intell Gastroenterol 2026; 7(2): 123751
- URL: https://www.wjgnet.com/2644-3236/full/v7/i2/123751.htm
- DOI: https://dx.doi.org/10.35712/aig.123751
Abdominal tuberculosis (ATB) encompasses intestinal, peritoneal, nodal, and solid-visceral forms and remains a major diagnostic and therapeutic challenge, particularly in high-burden low-income and middle-income countries (LMIC)[1]. According to the World Health Organization Global Tuberculosis Report 2025[2], an estimated 10.7 million people developed tuberculosis in 2024, with 1.23 million deaths; the South-East Asia, African, and Western Pacific regions account for the large majority of cases, and eight countries (led by India at approximately 25% of the global total) con
Evidence-based hierarchical criteria, such as those proposed by Jha et al[5] and operationalized in India’s - Indian Council of Medical Research (ICMR) standard treatment workflow (STW) for the management of adult ATB (2022)[6], integrate clinical, radiological, endoscopic, histopathological, and microbiological findings [including ascitic adenosine deaminase > 39 U/L, caseating granulomas, and mandatory human immunodeficiency virus (HIV) testing]. Yet resource constraints in endemic settings often limit their application, creating opportunities for artificial intelligence (AI) to support objective, rapid decision-making at key STW nodes (e.g., when diagnosis is unclear or response assessment at 4/8 weeks). AI and machine learning (ML) technologies have already proven transformative in pulmonary tuberculosis through computer-aided detection systems that improve sensitivity, objectivity, and workflow efficiency[7]. These same technologies offer objective, rapid, multimodal solutions capable of reducing subjectivity and supporting precision care across the diagnostic pathway for ATB. To date, however, no scoping review has synthesized AI and ML applications specifically for ATB diagnosis and differential diagnosis while critically appraising reporting quality, risk of bias and explicitly linking to existing clinical frameworks. We therefore conducted this Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews-compliant scoping review[8] to map the existing evidence, categorize applications by research theme and modality, appraise reporting quality, identify key gaps, and propose a translational roadmap for safe and equitable clinical integration that augments rather than replaces current evidence-based pathways.
This systematic review was conducted according to a pre-defined protocol following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines[8]. The review question was framed using the patient-centered care framework[9]: Population - patients with suspected or confirmed ATB; concept - applications of AI or ML; context - diagnosis or differential diagnosis vs Crohn’s disease, peritoneal carcinomatosis, lymphoma, or other mimics in any clinical setting.
Searches were conducted in PubMed [Medical Subject Heading (MeSH)-adapted], Cochrane Library, and Google Scholar. They were executed between April 8, 2026 and 10 April 10, 2026. Only English-language peer-reviewed articles were included, with no lower date limit. These databases were selected for their strengths: PubMed for comprehensive biomedical and clinical literature coverage, Cochrane Library for systematic reviews, and Google Scholar for broad scholarly indexing that includes recent preprints and regional publications.
The full reproducible PubMed search string was as follows: [“Artificial Intelligence” (MeSH) OR “Machine Learning” (MeSH) OR “Deep Learning” (MeSH) OR radiomics (tiab) OR CNN (tiab) OR XGBoost (tiab) OR “few-shot learning” (tiab) OR NLP (tiab)] AND [“Tuberculosis” (MeSH) OR “abdominal tuberculosis” (tiab) OR “intestinal tuberculosis” (tiab) OR “peritoneal tuberculosis” (tiab) OR “tuberculous peritonitis” (tiab) OR “gastrointestinal tuberculosis” (tiab)]. Equivalent adapted search strings were used in the Cochrane Library and Google Scholar.
Inclusion criteria comprised peer-reviewed original research or systematic reviews applying AI or ML to human ATB diagnosis or differential diagnosis. Exclusion criteria comprised exclusive pulmonary tuberculosis focus, no AI or ML component, animal or in vitro studies, editorials, letters, or conference abstracts without full data, and non-peer-reviewed material.
Two independent reviewers performed title/abstract and full-text screening using Rayyan[10]. Inter-rater agreement was substantial (Cohen’s κ = 0.82 for title/abstract screening; κ = 0.78 for full-text screening). Conflicts were resolved through discussion and consensus, with arbitration by the senior author when necessary. Data extraction used a stan
In addition to performance extraction, we performed a structured appraisal of reporting quality and risk of bias elements aligned with Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (for prediction model studies)[12], Minimum Information about Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist principles[13], and Prediction Model Risk of Bias Assessment Tool domains[14] where applicable. Common deficiencies were summarized narratively. Studies were thematically categorized as intestinal tuberculosis vs Crohn’s disease, peritoneal tuberculosis vs carcinomatosis, nodal tuberculosis vs lymphoma, multimodal integration, or novel/emerging approaches. No patients or members of the public were involved in the design, conduct, or reporting of this scoping review. This is common in early-stage scoping reviews of emerging technologies, where the primary aim is evi
We identified 227 records; after deduplication, 119 unique articles were screened. Seventy-six were excluded at title and abstract stage and 26 of the 43 full texts assessed were excluded (13 not ATB-specific and/or AI/ML applications specific, 6 insufficient data, 4 non-original, 3 pulmonary or unrelated). Seventeen studies met inclusion criteria: 16 original retrospective studies (published 2020-2025, with clear acceleration after 2021) and one international systematic review (Sachan et al[16], 2024) as summarized in Figure 1 and Table 1[16-32]. The Sachan et al's review[16] synthesized evidence on AI for discriminating Crohn disease from gastrointestinal tuberculosis up to early 2024 and highlighted generally good discriminatory performance of radiomics and deep-learning models while noting important limitations in study design and external validation[16].
| Ref. | Country | Design and sample | Target | Primary modality | AI/ML technique | Key performance | Explainability | Key notes |
| Park et al[17], 2025 | South Korea | Retrospective; internal 1132 CD + 1045 GITB images (130 points + 123 points; train/value/test), external 67 CD + 63 GITB images (17 points + 14 points, no overlap). Colonoscopy (white-light lesion images) | ITB vs CD (colonoscopic) | Colonoscopy (white-light lesion images) | Deep learning/CNN UNet-ResNet50 CNN (multitask learning) | Internal: Acc 97.7% (AUROC = 0.997). External: Acc 81.5% (AUROC = 0.877) | Human crossover trial (6 endoscopists, 780 interpretations): Pooled acc ↑86.2% to 88.8% (P = 0.010), trainees ↑76.7% to 81.0% (P = 0.002) | Most rigorous image-based study with external validation + human-AI crossover trial; demonstrates trainee benefit (+4.3% Acc) and addresses prior gaps in raw-image AI, generalizability, and clinical utility evaluation |
| Cheng et al[18], 2024 | China | Retrospective; 330 patients (value 1 = 224, value 2 = 106); SMOTE | CD vs ITB | CT enterography (arterial + venous phases) | DL radiomics + LASSO + logistic | Arterial-venous DL AUC = 0.885/0.877/0.800 (train/value 1/value 2) | None reported | Multi-phase fusion key; DL outperformed handcrafted in arterial phase |
| Shu et al[19], 2024 | China | Retrospective; 241 patients (51 parameters); real-world validation | ITB vs CD | Clinical/EHR/la | XGBoost + SHAP + LIME | AUC = 0.946; Acc 0.884; real-world Acc 0.860, Sens 0.833, Spec 0.871 | SHAP + LIME; top features T-SPOT, pulmonary TB | Strong MDT agreement (90.7%, kappa 0.78); ready for deployment |
| Lu et al[20], 2024 | China | Retrospective; 82 patients (25 ITB, 57 CD); train 54, test 28 | ITB vs CD | Multimodal (MRE radiomics + colonoscopy radiomics) | LASSO + DL + logistic; fused model (33 features) | Multidisciplinary AUC = 0.94 (test); single-modality 0.68-0.90 | None reported | First true multidisciplinary fusion; superior to all singles (DeLong P < 0.05) |
| Liu et al[21], 2024 | China | Retrospective; 85 cases (1973 WSI) | CD vs ITB (surgical specimens) | Histopathology whole-slide images | DL CNN; attention maps | Case-level AUC = 0.886 (train)/0.893 (test); slide-level 0.954/0.827 | Attention maps; top-10 features | Outperformed juniors; comparable to senior GI pathologists |
| Li et al[22], 2024 | China | Retrospective; 72 paraffin sections | CD vs ITB | ATR-FTIR spectroscopy (mid-IR on paraffin) | Spectroscopy + XGBoost/ML | High accuracy and specificity (detailed in full text) | None reported | Rapid chemical fingerprinting; non-destructive |
| Lin et al[23], 2024 | China | Retrospective; 122 endoscopic images (99 CD, 23 ITB) | ITB vs CD | White-light endoscopic (ileum) | Few-shot (2-way 3-shot; Xception + dual transfer) | AUC = 0.81 (dual transfer); better than single transfer (0.56) & endoscopists | None reported | Highly data-efficient for rare ITB; dual transfer learning |
| Shen et al[24], 2024 | China | Retrospective; 301 patients (107 ATB-LN + 194 lymphoma) | Nodal TB vs lymphoma | CECT (abdominal LNs, 3D VOI) | Radiomics (8 features); LR (primary) | LR AUC = 0.91 (train)/0.86 (value); Acc 88%/83%; Sens 76%/72% | None reported | Strong shape/texture signature; excluded small/necrotic nodes |
| Pang et al[25], 2023 | China (multicenter) | Retrospective; 178 patients (88 PTB + 90 CD) | PTB vs PC | CT (omental, peritoneal, ascites, LN signs) | ML ensemble on selected features | AUC = 0.971 (train)/0.914 (test); F1 0.923/0.867 | None reported | Multicentre; simple interpretable CT signs; strong performance |
| Gong et al[26], 2023 | China | Retrospective; 105 patients (61 CD, 44 ITB); 5-fold cross-validation (single-center) | ITB vs CD | CT enterography (multiregional radiomics) | Multiregional radiomics model (CT enterography-based) | Combined (nomogram) model: AUC = 0.975 (train 95%CI: 0.953–0.998)/0.958 (CV 95%CI: 0.925–0.991); Acc 89.5%; Sens 86.9%; Spec 93.2% (DeLong P =0.004 vs clinical). Radscore (VOI1+VOI2) AUC = 0.962/0.926 | Nomogram (visual) | Multiregional CTE radiomics (intestinal wall + largest LN) + clinical (involved segments) + endoscopy (longitudinal ulcer) via logistic nomogram; highest clinical benefit (DCA); Acc 89.5% superior to readers (66.7%-75.2%); good calibration; single-center Chinese cohort. Completes the 16 studies with visual nomogram for CD vs ITB differentiation |
| Lu et al[27], 2023 | China | Retrospective; 1271 EHR patients (875 CD, 396 ITB) | CD vs ITB (colonoscopy text) | Free-text EHR (NLP) | Debiased TextCNN (distilled; integrated gradients) | Acc 0.83 (noisy value 0.70); superior to non-debiased | Integrated gradients; 39 phrases (17 guideline-based) | Trustworthy AI (accuracy + interpretability + robustness) |
| Chen et al[28], 2022 | China | Retrospective; 287 cases (59 indicators) | ITB vs CD | Clinical + laboratory + endoscopic (multimodal) | Fusion correlation neural network | Average accuracy > 91% | Interpretable rules | Perianal fistulas, comb sign, cobblestone to CD; ascites to ITB |
| Weng et al[29], 2022 | China | Retrospective; 200 patients (160 CD, 40 ITB) | ITB vs CD | Clinical + radiological (9 variables) | XGBoost (best); SHAP | AUC = 0.891; Sens 0.813; Spec 0.969; MCC 0.801 | SHAP (global and local) | First SHAP use; perianal fistulas and comb sign point to CD |
| Zhu et al[30], 2021 | China | Retrospective; 160 patients (93 CD, 67 ITB) | CD vs ITB (ileocecal) | CT enterography radiomics + clinical | Radiomics (9 GBDT features) + 2 clinical | AUC = 0.96 (train)/0.93 (value); superior to singles (P < 0.01) | Nomogram (visual) | Excellent bedside translation; helps treatment decisions |
| Kim et al[31], 2021 | South Korea | Retrospective; 6617 colonoscopy images (211 CD, 299 BD, 217 ITB) | 3-class CD vs BD vs ITB (pairwise) | Colonoscopy images (white-light) | DL CNN; Grad-CAM | 3-class Acc 65.15% (all)/72.01% (typical); AUROC = 0.78-0.86 | Grad-CAM | Best on typical images; helpful for inexperienced endoscopists |
| Tong et al[32], 2020 | China | Retrospective; 6,399 patients (5,128 UC, 875 CD, 396 ITB) | 3-class UC vs CD vs ITB (pairwise) | Free-text endoscopic reports (NLP) | RF and TextCNN | RF: UC-CD 0.89/0.84, UC-ITB 0.83/0.82, CD-ITB 0.72/0.77 (sens/spec) | None reported | Largest NLP cohort; excellent for inexperienced clinicians |
Our review incorporates five additional original studies published after the Sachan et al's search window[16] and applies a structured appraisal of reporting quality and risk-of-bias elements not previously undertaken (Figure 2). Risk of bias assessment revealed that most studies were at high or unclear overall risk, driven by retrospective single-center designs, composite reference standards, and limited external validation. Only a minority of recent studies incorporated external validation or real-world testing. Geographic origin of original studies (Figure 3) was China (n = 14/16, 87.5%) and South Korea (n = 2, 12.5%). Sample sizes ranged from 66 to 6617 patients or images.
Of the 16 original studies, 14 focused primarily on differentiating intestinal tuberculosis from Crohn’s disease, while one each addressed peritoneal tuberculosis vs carcinomatosis and nodal tuberculosis vs lymphoma (Table 2). Four studies incorporated multimodal integration, and several explored emerging techniques, including few-shot learning, spectro
| Theme | Imaging/point data | Clinical/areal data | Key techniques | Representative original studies (n = 16) |
| Intestinal tuberculosis vs Crohn’s disease | Endoscopy images (white-light colonoscopy), CT/MRE radiomics, histopathology WSI, ATR-FTIR spectroscopy | Clinical/EHR parameters, nomograms, T-SPOT, pulmonary TB history | CNN/deep learning (U-Net, ResNet), radiomics (handcrafted + deep learning), XGBoost, few-shot learning (Xception), NLP/TextCNN (debiased), multimodal fusion, SHAP/LIME/attention maps explainability | Park et al[17], 2025; Cheng et al[18], 2024; Shu et al[19], 2024; Liu et al[21], 2024; Li et al[22], 2024; Lin et al[23], 2024; Gong et al[26], 2023; Lu et al[27], 2023; Chen et al[28], 2022; Weng et al[29], 2022; Zhu et al[30], 2021; Kim et al[31], 2021; Tong et al[32], 2020 |
| Peritoneal tuberculosis vs carcinomatosis | CT (omental/peritoneal signs, ascites, lymph nodes, scalloping) | Omental/peritoneal imaging signs | Machine learning ensembles, radiomics (shape, first-order, texture features) | Pang et al[25], 2023 |
| Nodal tuberculosis vs lymphoma | Contrast-enhanced CT (lymph node features) | Lymph-node morphological/textural features | Radiomics (logistic regression/SVM) | Shen et al[24], 2024 |
| Multimodal integration | Fusion of MRE radiomics + colonoscopy images + histopathology WSI | Integrated clinical + endoscopic + radiological + pathological features | Multidisciplinary fusion models (LASSO + logistic regression), fusion correlation neural networks | Lu et al[20], 2024; Chen et al[28], 2022 |
| Novel/emerging approaches | Few-shot endoscopic images, ATR-FTIR spectroscopy, noisy EHR text | Clinical parameters (for few-shot/explainable models) | Few-shot learning (dual transfer), spectroscopy + XGBoost/ML, debiased TextCNN (NLP), explainable AI (SHAP, LIME, integrated gradients, attention maps) | Lin et al[23], 2024; Li et al[22], 2024; Lu et al[27], 2023 (also embedded in multiple intestinal TB vs CD studies above) |
Overall performance reached AUC = 0.80-0.96 and accuracy 69%-100% in optimized models. Multimodal and explainable models consistently outperformed single-modality approaches. Temporal publication trends (2020-2025) and modality distribution is shown in Figure 4.
Radiomics (computed tomography/magnetic resonance elastography): Strong performance across studies. Zhu et al[30] (2021) achieved AUC = 0.96 (train)/0.93 (validation) using computed tomography enterography radiomics plus clinical variables with a visual nomogram. Cheng et al[18] (2024) reported arterial-venous deep learning radiomics AUCs of approximately 0.88. Multiregional radiomics approaches further improved discrimination.
Endoscopic deep learning/convolutional neural network: High internal accuracy with emerging external validation. Park et al[17] (2025) demonstrated a multitask UNet-ResNet50 convolutional neural network (CNN) on white-light colo
Multimodal and explainable AI: Consistently superior. Lu et al[20] (2024) multidisciplinary fusion model (magnetic resonance elastography radiomics + colonoscopy radiomics) achieved AUC = 0.94 on the test set, significantly outperforming single-modality models. Shu et al’s XG Boost model (2024)[19] on 51 clinical/electronic health record parameters reached AUROC = 0.946 (accuracy 0.884), with real-world validation accuracy 0.860, sensitivity 0.833, and specificity 0.871; strong agreement with multidisciplinary teams (90.7%, κ = 0.78) and interpretable via SHapley Additive exPlanations/local interpretable model-agnostic explanations (top features: T-SPOT, pulmonary tuberculosis history).
Histopathology whole-slide imaging: Liu et al's deep learning model (2024)[21] on 1973 whole-slide images achieved case-level AUC = 0.886 (internal)/0.893 (external) and outperformed junior pathologists while matching experienced gastrointestinal pathologists. Attention maps provided interpretability.
Emerging approaches: Few-shot learning (Lin et al[23], 2024) offered data-efficient classification for rare intestinal tuberculosis cases. attenuated total reflection-Fourier transform infrared spectroscopy spectroscopy combined with ML (Li et al[22], 2024) provided rapid, non-destructive chemical fingerprinting. Debiased Text CNN (Lu et al[27], 2023) improved robustness on noisy electronic health record text.
Performance was highest in multimodal and explainable models. Standout examples include Park et al[17] (2025) endoscopic multitask CNN with external validation and demonstrated improvement in endoscopist accuracy, and Liu et al[21] (2024) whole-slide imaging deep learning that matched expert gastrointestinal pathologists. These findings are consistent with the performance range summarized by Sachan et al[16] (2024), although multimodal and explainable approaches have shown further gains in more recent work.
Most studies relied on composite clinical criteria rather than microbiological confirmation as the primary reference standard. This raises the risk that models may replicate clinician diagnostic bias rather than true biological distinctions, particularly when aligned against hierarchical criteria that prioritize microbiological or caseating granuloma evidence[32,33]. No studies stratified performance by HIV status, immunosuppression, or drug-resistant tuberculosis. Solid-organ visceral ATB forms remained largely unaddressed. These gaps directly inform the priorities of the translational roadmap below.
Sachan et al[16] (2024) reached broadly similar conclusions regarding the promise of radiomics and endoscopic deep learning for intestinal tuberculosis vs Crohn disease differentiation. However, their review did not assess reporting qua
The overwhelming predominance of East Asian cohorts (100% of the 16 original studies) means that the synthesized performance metrics and identified best practices primarily reflect populations with relatively lower HIV co-infection rates and potentially different mycobacterial/host factors compared with South Asian, African, or Latin American high-burden settings. This geographic concentration limits confident extrapolation and reinforces the roadmap’s call for urgent prospective validation in diverse LMIC populations. Small ATB subsample sizes in several studies further elevate overfitting risk, as already flagged in the risk-of-bias appraisal. The current scoping review therefore provides both updated evidence maps and a more granular appraisal of methodological and equity considerations.
This scoping review of 17 studies demonstrates that AI and ML can substantially enhance the differentiation of ATB from its major mimics. Radiomics based on computed tomography or magnetic resonance enterography, together with endo
AI and ML are particularly valuable because conventional tests have well-documented limitations. Xpert Mycobacterium tuberculosis/rifampicin sensitivity remains low on intestinal tissue (approximately 23% vs composite reference) and ascitic fluid (approximately 30%-64% vs culture)[34]. Culture yields are modest (approximately 35%-40%)[35]. Histological features such as caseating granulomas offer high specificity but low sensitivity (approximately 21%), while confluent granulomas and histiocyte-lined ulcers provide supportive but imperfect discrimination from Crohn’s disease[36]. Imaging signs show significant overlap. These constraints, emphasized in evidence-based hierarchical frameworks[5], create precisely the subjectivity and diagnostic uncertainty that well-designed AI tools can mitigate.
Overwhelmingly retrospective, single-center designs (predominantly East Asian cohorts) introduce spectrum bias and raise concerns about overfitting with small intestinal tuberculosis samples. Transportability of China-trained models to Indian, African, and other populations remains untested. These populations differ in HIV prevalence, malnutrition rates, and potentially mycobacterial strain characteristics, raising substantive equity concerns[37]. Infrastructure gaps in remote LMIC clinics (reliable internet, electronic health record linkage, offline capability) are unresolved[38]. Tuberculosis-related stigma and patient privacy concerns require explicit attention in deployment frameworks[39].
While AI and ML tools can improve ATB differentiation, they carry several risks. False-negative predictions may delay anti-tubercular therapy in true cases, allowing disease progression and continued transmission[34]. False-positive outputs risk exposing patients with Crohn’s disease or other mimics to unnecessary anti-tuberculosis treatment or corticosteroids[36]. Overreliance on algorithmic outputs, especially among trainees, may erode clinical judgment and diagnostic skills when AI recommendations conflict with nuanced findings or hierarchical criteria[5]. Automated labeling also risks amplifying tuberculosis-related stigma[39]. These harms can be mitigated through mandatory human-in-the-loop design, built-in explainability features, prospective safety monitoring (including adverse-event tracking and subgroup analyses) embedded in phase 2 and phase 3 studies of the translational roadmap, and explicit alignment of AI outputs with ICMR STW decision nodes so that tools support rather than supplant established evidence-based pathways[6,14].
AI and ML should be positioned as adjuncts that augment, rather than replace, established hierarchical diagnostic criteria and objective response assessment (early mucosal healing at two months, ascites resolution, fecal calprotectin trends)[40]. Pulmonary tuberculosis AI applications (particularly chest X-ray computer-aided detection) are more mature[41]; abdo
Both this review and the earlier systematic review by Sachan et al[16] (2024) underscore that AI applications for ATB remain at an early stage. In contrast, computer-aided detection systems for pulmonary tuberculosis are considerably more mature. While Sachan et al[16] focused on diagnostic discrimination performance, the present work additionally empha
AI and ML offer the potential to improve diagnostic precision in ATB and support timely initiation of anti-tubercular therapy. However, these technologies are best positioned as adjuncts that augment clinical judgment, microbiological testing, radiological expertise, and histopathology, rather than as replacements[42]. ML-based predictive models can estimate the probability of disease and thereby reduce diagnostic delays, while AI-assisted triage systems may help prioritize high-risk patients for early investigations in resource-constrained settings[43]. In addition, the ability of these tools to analyze large datasets presents opportunities for the discovery of novel biomarkers and phenotypic subtypes of ATB[44].
To translate promising retrospective models into equitable clinical impact, we propose a structured four-phase translational roadmap with defined timelines, success metrics, and multi-stakeholder engagement as illustrated in Figure 5.
Phase 1 foundation (2020-2026): This phase involves retrospective model development, internal validation, open dataset creation, and standardized reporting in accordance with the MI-CLAIM checklist[16]. To strengthen the evidence base for future research, we recommend establishing a minimal core dataset for all ATB-AI studies. This should include demo
Phase 2 prospective validation (2026-2028): Phase 2 focuses on generating robust, generalizable evidence through multicenter prospective cohorts across diverse LMIC settings. We recommend establishing at least six sites (India, South Africa, Indonesia, Nigeria, Brazil, and Pakistan), with a minimum of 400 participants per site and adequate power to evaluate performance in HIV-positive and drug-resistant tuberculosis subgroups. Studies should incorporate real-world variability, rigorous external validation, head-to-head comparison against current Jha et al’s criteria[5] and ICMR STW pathways[6], and prospective assessment of diagnostic yield. The primary endpoint will be diagnostic accuracy (AUC, sensitivity, and specificity). Secondary endpoints include reduction in diagnostic delay, rates of unnecessary laparoscopy, and cost per correct diagnosis. Federated learning combined with systematic bias audits assessing demographic parity across ethnicity and HIV status is strongly encouraged[45]. Success will be defined as achieving an external validation AUC ≥ 0.85 in diverse LMIC cohorts with a clinically meaningful reduction in diagnostic delay.
Phase 3 clinical integration (2028-2031): This phase centers on the development and deployment of practical, regulatory-approved AI tools designed to integrate seamlessly into existing national diagnostic workflows. Priority tools include real-time endoscopic AI systems, multimodal decision-support applications, explainable radiomics nomograms, and treatment-response predictors[43]. These should support key decision nodes within the ICMR STW[6], such as real-time assistance when diagnosis remains uncertain, prediction of treatment response at 4-week and 8-week assessments, and automated flagging for surgical or endoscopic intervention. Regulatory approval pathways should be pursued through the Central Drugs Standard Control Organization (India), the United States Food and Drug Administration, and Conformité Européenne marking for software as a medical device[46]. Implementation must include structured clinician training, seamless electronic health record integration, and robust consent and privacy frameworks that address tuberculosis-related stigma[39]. Success metrics include at least 80% clinician adoption in pilot sites within 12 months and a minimum 50% reduction in inappropriate anti-tuberculosis therapy or steroid exposure among patients ultimately diagnosed with Crohn’s disease or other mimics. Economic evaluations embedded in phase 3 will model modality-specific cost drivers (imaging and endoscopy AI infrastructure vs downstream savings from reduced unnecessary laparoscopy and surgery, historically occurring in approximately 15% of cases) alongside diagnostic accuracy gains[5]. Key partners should include National Tuberculosis Programs, the Stop Tuberculosis Partnership, the foundation for innovative new diagnostics, and patient advocacy groups.
Phase 4 implementation and equity (2031 and beyond): The final phase emphasizes large-scale, sustainable implemen
This roadmap provides a pragmatic, equity-centered pathway from current retrospective evidence to real-world deployment, with the explicit goal of complementing and strengthening existing evidence-based frameworks rather than creating parallel systems. Feasibility of the proposed phases will be enhanced by alignment with established governance frameworks of National Tuberculosis Programs (e.g., India’s NTEP), ethics harmonization via national research councils, and data-sharing and capacity-building mechanisms facilitated by the foundation for innovative new diagnostics and the Stop Tuberculosis Partnership[6,47].
Most included studies did not report detailed quantitative imaging features (such as bowel wall thickness, number of necrotic lymph nodes, or extent of ileocecal and peritoneal involvement) or correlate these with AI model performance. Furthermore, large multicontinental, prospectively collected datasets with microbiological confirmation are urgently needed before robust predictive models can be developed and integrated with clinical, biomarker, and histopathological data. Although Google Scholar was included to broaden coverage, the restriction to English-language publications may have missed relevant studies published in regional journals or non-English databases (e.g., China National Knowledge Infrastructure, Latin American and Caribbean Health Sciences Literature). Future updates of this review should incor
AI and ML offer a powerful, objective adjunct for ATB diagnosis and differential diagnosis. By systematically addressing the identified gaps through the proposed translational roadmap, these technologies can reduce diagnostic uncertainty and minimize inappropriate therapy and surgery. The emphasis must be on prospective, diverse, explainable, and imple
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