Akshintala, Venkata

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide, accounting for approximately 10% of all cancer cases. Despite the proven effectiveness of conventional screening modalities such as colonoscopy and fecal immunochemical testing (FIT), their invasive nature, high cost, and limited patient compliance hinder widespread adoption. Recent advancements in artificial intelligence (AI) and bowel sound-based signal processing have enabled non-invasive approaches for gastrointestinal diagnostics. Among these, bowel sound analysis—historically considered subjective—has reemerged as a promising biomarker using digital auscultation and machine learning. Objective: This review explores the potential of AI-powered bowel sound analytics for early detection, screening, and characterization of colorectal cancer. It aims to assess current methodologies, summarize reported performance metrics, and highlight translational opportunities and challenges in clinical implementation. Methods: A narrative review was conducted across PubMed, Scopus, Embase, and Cochrane databases using the terms colorectal cancer, bowel sounds, phonoenterography, artificial intelligence, and non-invasive diagnosis. Eligible studies involving human bowel sound-based recordings, AI-based sound analysis, or machine learning applications in gastrointestinal pathology were reviewed for study design, signal acquisition methods, AI model architecture, and diagnostic accuracy. Results: Across studies using convolutional neural networks (CNNs), gradient boosting, and transformer-based models, reported diagnostic accuracies ranged from 88% to 96%. Area under the curve (AUC) values were ≥0.83, with F1 scores between 0.71 and 0.85 for bowel sound classification. In CRC-specific frameworks such as BowelRCNN, AI models successfully differentiate abnormal bowel sound intervals and spectral patterns associated with tumor-related motility disturbances and partial obstruction. Distinct bowel sound-based signatures—such as prolonged sound-to-sound intervals and high-pitched “tinkling” proximal to lesions—demonstrate the physiological basis for CRC detection through bowel sound-based biomarkers. Conclusions: AI-driven bowel sound analysis represents an emerging, exploratory research direction rather than a validated colorectal cancer screening modality. While early studies demonstrate physiological plausibility and technical feasibility, no large-scale, CRC-specific validation studies currently establish sensitivity, specificity, PPV, or NPV for cancer detection. Accordingly, bowel sound analytics should be viewed as hypothesis-generating and potentially complementary to established screening tools, rather than a near-term alternative to validated modalities such as FIT, multitarget stool DNA testing, or colonoscopy.

AbstractBackground and AimThe primary aim was to validate the Pancreatitis Activity Scoring System (PASS) in a multicenter prospectively ascertained acute pancreatitis (AP) cohort. Second, we investigated the association of early PASS trajectories with disease severity and length of hospital stay (LOS).MethodsData were prospectively collected through the APPRENTICE consortium (2015–2018). AP severity was categorized based on revised Atlanta classification. Delta PASS (ΔPASS) was calculated by subtracting activity score from baseline value. PASS trajectories were compared between severity subsets. Subsequently, the cohort was subdivided into three LOS subgroups as short (S‐LOS): 2–3 days; intermediate (I‐LOS): 3–7 days; and long (L‐LOS): ≥7 days. The generalized estimating equations model was implemented to compare PASS trajectories.ResultsThere were 434 subjects analyzed including 322 (74%) mild, 86 (20%) moderately severe, and 26 (6%) severe AP. Severe AP subjects had the highest activity levels and the slowest rate of decline in activity (P = 0.039). Focusing on mild AP, L‐LOS subjects (34%) had 28 points per day slower decline; whereas, S‐LOS group (13%) showed 34 points per day sharper decrease compared with I‐LOS (53%; P < 0.001). We noticed an outlier subset with a median admission‐PASS of 466 compared with 140 in the rest. Morphine equivalent dose constituted 80% of the total PASS in the outliers (median morphine equivalent dose score = 392), compared with only 25% in normal‐range subjects (score = 33, P value < 0.001).ConclusionsThis study highlighted that PASS can quantify AP activity. Significant differences in PASS trajectories were found both in revised Atlanta classification severity and LOS groups, which can be harnessed in AP monitoring/management (ClincialTrials.gov number, NCT03075618).