---
title: "DenseNet-121 Diagnostic Accuracy for Chest Radiograph Triage in Nepali Health Assessments"
id: "bmj-open-8-diagnostic-accuracy-of-a-densenet-121-deep-learning-algorithm-for-chest"
canonical_url: "https://medichelpline.com/clinical-feed/bmj-open-8-diagnostic-accuracy-of-a-densenet-121-deep-learning-algorithm-for-chest"
content_type: "clinical_feed_article"
specialty: "Radiology"
source_name: "BMJ Open"
source_url: "http://bmjopen.bmj.com/cgi/content/short/16/10/e124868?rss=1"
published_at: "2026-10-01T11:56:34.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# DenseNet-121 Diagnostic Accuracy for Chest Radiograph Triage in Nepali Health Assessments
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/bmj-open-8-diagnostic-accuracy-of-a-densenet-121-deep-learning-algorithm-for-chest
- **Specialty:** [Radiology](https://medichelpline.com/clinical-feed/radiology.md)
- **Primary Source:** BMJ Open
- **Source URL:** [Original Journal Publication](http://bmjopen.bmj.com/cgi/content/short/16/10/e124868?rss=1)
- **Published At:** 2026-10-01T11:56:34.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Prospective, single-centre shadow-mode diagnostic accuracy study evaluated a publicly available **DenseNet-121** convolutional neural network (TorchXRayVision) for triaging chest radiographs of health-assessment applicants in Nepal. - Study conducted at Patan Academy of Health Sciences/Patan Hospital radiology department between 5 June 2026 and 20 June 2026; 826 consecutive applicants imaged, two excluded for DICOM failure. - Reference standard: single-reader-per-case review by one of three radiologists (two board-certified, one resident) blinded to AI output; binary normal/abnormal classification recorded. - Index test: DenseNet-121 (densenet121-res224-all pretrained weights); a single aggregated pathology probability score per radiograph used; post hoc threshold 0.6258 selected to meet prespecified ≥95% sensitivity criterion. - At threshold 0.6258, performance: sensitivity 95.12% (95% CI 83.9–98.7%), specificity 77.2% (95% CI 74.1–80.0%), AUC 0.9583 (95% bootstrap CI 0.9225–0.9843). - Predictive values: negative predictive value (NPV) 99.67% (95% Wilson CI 98.8–99.9%), positive predictive value 17.89% (95% Wilson CI 13.4–23.5%). Cohen’s kappa 0.237 (95% bootstrap CI 0.174–0.304). - Calibration issues: Brier score 0.3621, expected calibration error 0.564, and score compression (score range 0.52–0.72) indicate calibration failure despite preserved discrimination. - Cross-validation (10-fold) produced bias-corrected sensitivity 95.12% and specificity 75.80%, showing minimal optimism and internal validation that metrics were not materially inflated by circular optimisation; no external validation performed. - Study cautions: small number of reference-standard positives (n=41) yields wide CI for sensitivity; results support potential use as a **radiographic rule-out triage** tool but do not exclude microbiological tuberculosis. Local prospective calibration and independent external validation recommended before deployment.
## Clinical Analysis & Structured Key Points
Objectives To evaluate the diagnostic accuracy of a publicly available DenseNet-121 convolutional neural network (TorchXRayVision) for triaging chest radiographs of health assessment applicants at a tertiary hospital in Nepal. Design Prospective, single-centre, shadow-mode diagnostic accuracy validation study. Reported in accordance with the Standards for Reporting of Diagnostic Accuracy Studies (STARD) 2015 checklist and STARD-Artificial Intelligence (AI)/Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI) guidelines. Setting Department of Radiology and Imaging, Patan Academy of Health Sciences/Patan Hospital, Lalitpur, Nepal. Participants 826 consecutive health assessment applicants (foreign employment predeparture medical examination and student migration) undergoing chest radiography from 5 June 2026 to 20 June 2026 inclusive (16 days). Two cases were excluded due to Digital Imaging and Communications in Medicine technical failure. Index test DenseNet-121 algorithm (TorchXRayVision library, densenet121-res224-all pretrained weights). A maximum aggregated pathology probability score was derived per radiograph and compared against a post hoc derived threshold of 0.6258 (selected as the highest threshold achieving the prespecified ≥95% sensitivity criterion). Reference standard Single-reader-per-case review by one of three radiologists - two board-certified radiodiagnosticians (LS: 276 cases; DM: 275 cases) and one radiology resident (UB: 275 cases) - each blinded to AI output, using a standardised data collection worksheet capturing binary classification (abnormal/normal) and free-text findings. Results Of 826 radiographs, 41 (4.97%) were classified as abnormal by the reference standard. At the post hoc derived threshold of 0.6258, the DenseNet-121 algorithm achieved: sensitivity 95.12% (95% CI 83.9% to 98.7%), specificity 77.2% (95% CI 74.1% to 80.0%), area under the receiver operating characteristic curve 0.9583 (95% bootstrap CI 0.9225 to 0.9843), negative predictive value (NPV) 99.67% (95% Wilson CI 98.8% to 99.9%), positive predictive value 17.89% (95% Wilson CI 13.4% to 23.5%) and Cohen's 0.237 (95% bootstrap CI 0.174 to 0.304). Brier score was 0.3621 (null Brier 0.0472) and expected calibration error was 0.564, confirming calibration failure due to score compression (range 0.52 - 0.72) despite preserved discrimination. The sensitivity estimate should be interpreted with caution given the relatively small number of reference-standard positives (n=41); the Wilson CI width of 14.8 percentage points (83.9 - 98.7%) reflects substantial uncertainty around this point estimate. Cross-validated results 10-fold cross-validation yielded bias-corrected sensitivity 95.12% (95% Wilson CI 83.9% to 98.7%; optimism 0.00 pp) and specificity 75.80% (95% Wilson CI 72.7% to 78.7%; optimism+1.40 pp), confirming by internal validation that primary metrics are not materially inflated by circular optimisation; independent external validation was not performed. Conclusions The DenseNet-121 algorithm demonstrated high point-estimate sensitivity and excellent discrimination for chest radiograph triage in a Nepali health-assessment population, supporting its potential as a radiographic abnormality rule-out triage tool (NPV 99.67%); this does not constitute microbiological exclusion of active pulmonary tuberculosis. Systematic score compression - preserved discrimination despite calibration shift - is a quantifiable marker of low- and middle-income country distributional shift. Prospective local calibration studies and independent external validation are warranted before operational deployment.
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