The cohort comprised patients who met the following criteria: (1) first PTB diagnosis between January 2004 and June 2023, (2) underwent sputum examinations, (3) CXR performed within –60 to +180 days from the initial diagnosis date, (4) at least 1 positive sputum sample (smear or culture), and (5) received a successful 6-month treatment course (no treatment after the seventh month).

For positive labels, infectious CXR images were defined as those obtained from the initial diagnosis date to the last positive sputum test result. For negative labels, we used CXR images obtained after the conversion date, which was the day when the sputum test result changed from positive to negative. Specifically, a 1-month gray zone was established following the next sputum test date after the conversion date, that is, the CXR image obtained after the second negative sputum test result post conversion was classified as noninfectious.

We used data from Severance Hospital for the training dataset. Internal validation was performed using temporal validation, and external validation was conducted using data from Yongin Severance Hospital, which has different geographical and scale characteristics compared with Severance Hospital. The data of patients with PTB who visited Severance Hospital, which is a 2437-bed tertiary teaching hospital in Seoul, between 2004 and 2020 (31,260 CXR images) were used for model training. Subsequently, data from patients who visited in 2021 (1855 CXR images) were used for testing, while data from those who visited between 2022 and 2023 (3027 CXR images) were used for internal validation. The total data included 4492 patients and 36,142 CXR images (18,557 negative and 17,585 positive). In addition, data from Yongin Severance Hospital, which is a separate 708-bed general hospital located in Yongin, were used for external validation. These included 299 patients and 1978 CXR images, with 863 negative and 1115 positive cases. Figure 1 illustrates the selection process, highlighting the distinct contributions of each institution.

We also used a lung segmentation dataset comprising 704 CXR images, sourced from the National Library of Medicine, National Institutes of Health, United States [16], and the Third People's Hospital of Shenzhen, China [17]. We divided the dataset into 3 subsets, allocating 60% for training, 20% for testing, and 20% for validation.

We developed a lung segmentation model based on TransUNet [18] to focus the analysis on lung parenchyma in the CXR images. Figure 2 shows an example CXR image, where only the lung region is segmented. Following segmentation, only the cropped lung regions were used in the analysis. Further details regarding the segmentation model are provided in Multimedia Appendix 1.

We selected the DenseNet121 [19] architecture, which was pretrained using the CheXNet [20] dataset, for developing an infectivity evaluation model. Detailed information regarding the infectivity evaluation model is provided in Multimedia Appendix 2.

In addition, we incorporated gradient-weighted class activation mapping [21]. It visually illustrates the CXR sections on which the neural network focuses during the prediction process through a heat map, highlighting the most influential areas for the predictions. Furthermore, we used local interpretable model-agnostic explanations [22] to segment the CXR images and elucidate the regions that contributed most significantly to its predictions, thereby providing additional insights into its decision-making process.

This study presents the quantitative data as mean values with SD and categorical data as frequencies and percentages.

In the baseline characteristics, for the analysis of categorical variables, the Fisher exact test was used for “Leukemia” only, whereas the remaining variables were statistically tested using the chi-square test. In addition, “Age” was tested through the Mann-Whitney U test. In all statistical tests, a P value of <.05 was considered statistically significant. The accuracy, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the performance of the AI model. We used 1000 bootstrap iterations to compute the 95% CIs for the above metrics.

All analyses were performed using Python (version 3.10.9; Python Software Foundation), TensorFlow-GPU (version 2.10.0; Google), and R (version 4.3.1; R Foundation for Statistical Computing).

This study adhered to the Declaration of Helsinki guidelines and was approved by the institutional review board of Severance Hospital (4-2023-0679). Owing to its retrospective design, the requirement for individual patient consent was waived by the institutional review board. In addition, we adhered to the Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View [23] throughout our study.

The baseline characteristics of the patients from both hospitals are summarized in Table 1. The proportion of male patients was slightly higher at Severance Hospital (2718/4492, 60.51%) than at Yongin Severance Hospital (178/299, 59.53%), although the difference was not statistically significant (P=.91). In addition, the average ages of patients at Severance Hospital and Yongin Severance Hospital were 65 (SD 18.95) years and 66.14 (SD 18.84) years, respectively. This difference in age distribution was not statistically significant (P=.22). However, there were significant differences in the prevalence of certain conditions. Chronic obstructive pulmonary disease was more prevalent in the Yongin Severance Hospital group (89/299, 29.77%) than in the Severance Hospital group (914/4492, 20.34%), with a P value of <.001.

Similarly, the prevalence of lung cancer and all cancer types was significantly higher in the Severance Hospital group (lung cancer: 255/4492, 5.68%; all cancers: 1200/4492, 26.71%) than in the Yongin Severance Hospital group (lung cancer: 4/299, 1.34%; all cancers: 28/299, 9.36%), both with P values of <.001. Other conditions such as drug-induced interstitial lung disease, connective tissue diseases, end-stage renal disease, and diabetes showed no statistically significant differences between the 2 groups (all P>.05). In addition, the prevalence of leukemia and pulmonary edema was similar between the 2 groups (leukemia: Severance Hospital 73/4492, 1.63%; Yongin Severance Hospital 6/299, 2%; pulmonary edema: Severance Hospital 78/4492, 1.74%; Yongin Severance Hospital 4/299, 1.34%), with P values of .78. Overall, these findings suggest that although there were some statistically significant differences in the prevalence of certain conditions between the patients from the 2 hospitals, other baseline characteristics such as sex, age, and the prevalence of several other conditions were similar.

The proposed deep learning model that uses CXR images to evaluate PTB infectivity can facilitate clinical decisions regarding quarantine or discharge as well as assist in evaluating the success of antituberculosis treatments. Furthermore, the ability to differentiate active lesions from healed, old lesions or those being healed can help to avoid unnecessary treatments or evaluations of inactive lesions, which is often an issue in countries with high PTB incidence rates [25,26].

CXR is an important tool for PTB screening, and the World Health Organization recommends its use for this purpose. However, many countries with a high PTB burden have a shortage of trained CXR specialists [27-29]. Therefore, AI-based screening of active PTB could provide these regions with a useful tool to protect public health and enhance PTB control. In addition, according to Kazemzadeh et al [27], deep learning–based, automatic, active PTB screening tools have the potential to offer significant cost savings of 40% to 80% in settings with a prevalence of 1% to 10%.

Furthermore, in countries with moderate to high PTB prevalence, it is common to encounter patients who have been treated or who have spontaneously healed from PTB [28]. Our study focused on distinguishing active, infective PTB, thereby equipping such countries with a more usefully adjusted tool for better PTB control.

Patients who are smear or culture positive for PTB are isolated until their sputum microbiological tests return negative, which can take several weeks for confirmation [30]. Our infectivity-targeted model offers the potential for immediate assessment, which could facilitate quicker decisions regarding discharge from the hospital or return to the workplace, potentially benefiting the socioeconomic situation of the region.

The proposed model exhibited consistent and reliable performance in both internal and external validations, highlighting its clinical applicability for determining PTB infectivity. By achieving a crucial balance between sensitivity and specificity, it effectively differentiated infective and noninfective cases, which holds significant clinical importance. Moreover, the integration of visualization techniques improved its transparency by elucidating the specific areas that influenced its decisions. Thus, the proposed model can aid in PTB management and assessment by leveraging broadly accessible CXR images.

Traditionally used smear tests have low sensitivity, which can lead to missed diagnoses, especially in cases of early infections with low bacterial loads. Although culture tests are considered standard, they can be slow, requiring several weeks to produce results. Thus, diagnostic decision-making must be improved through more efficient methods. Table 3 lists the benefits and limitations of the smear, culture, and CXR tests, providing insights into their performance, speed, and overall efficacy for diagnosing PTB. Evidently, the proposed CXR test can produce diagnosis results within several seconds, enabling rapid decision-making for quarantine. Moreover, it is noninvasive, cost-effective, and widely accessible and features minimal patient discomfort. In addition, advances in AI have enabled automated analyses, which reduce clinician workloads and improve diagnostic accuracy.

CT has also been used to differentiate PTB cases. Gao et al [33] developed and evaluated TBINet, a deep learning model that uses CT images to determine the infectivity of patients with PTB. It achieved AUROC scores of 0.82 and 0.75 for the validation and external validation sets, respectively, thereby outperforming traditional deep learning methods. However, the limitations of CT include high costs, limited facilities, and radiation exposure [34]. Our approach overcomes these limitations by using the more accessible and safer CXR, which is performed similarly to CT-based models. Choi et al [35] and Lee et al [28] have demonstrated the potential of CXR to effectively distinguish between patients with active PTB and healthy patients. Their findings, highlighted by AUROC scores ranging from 0.83 to 0.98, underscored the high accuracy of CXR-based techniques for identifying PTB. Existing studies have primarily focused on using AI for the diagnosis of PTB using CXR [36,37], but the potential of AI to monitor the progress of patients undergoing treatment has been relatively less explored. Unlike the studies mentioned above, which compared healthy patients against patients with PTB, our study differentiated between infectious and noninfectious cases to assess infectivity. Our study proposes the potential use of AI models to analyze CXR images of patients undergoing treatment, enabling the monitoring of treatment progress. In addition, our model can aid in determining the appropriate timing for lifting unquarantined measures. By assessing whether a patient remains infectious, health care providers can make more informed decisions about when it is safe to release patients from quarantine. This not only helps prevent the disease transmission but also reduces the psychological and social burden on patients who may otherwise face longer periods of quarantine than necessary.

Our model facilitates faster decision-making by capitalizing on the quick and accessible nature of CXR, which allows imaging within minutes, and the capability of the proposed model to evaluate CXR images in less than a second. This rapid turnaround time can lead to faster patient discharge or quarantine decisions, emphasizing its efficiency and effectiveness in clinical settings. Given the distinct challenges addressed in this study, comparing our AUROC scores directly with those of other studies may not fully capture its unique value and contributions, which lie in its innovative focus on distinguishing between PTB infectivity stages, rather than merely identifying active cases.

In addition, this study focused on the lung regions in CXR images and used a refined method for segmentation and cropping to accurately target the areas of interest. Thus, it not only improves the diagnostic accuracy by providing a clearer view of the lung regions but also significantly minimizes the impact of irrelevant factors on the decision-making process of the AI model. Building on the groundwork laid by Zaidi et al [38], who showed that lung segmentation can enhance the classification results of CXR analysis by reducing noise and improving efficiency, this study achieved further performance enhancements by exclusively concentrating on segmented and cropped lung areas. Thus, it demonstrated the substantial potential of focused image analysis for improving the accuracy and reliability of AI-driven diagnostics using medical images.

Moreover, our study used large-scale data that were collected over a broad time span, from 2004 to 2023, which was advantageous for analyzing temporal changes and trends in PTB diagnoses and treatments. In addition, data from almost 2 decades allowed us to draw more accurate and reliable conclusions, providing a robust foundation for the AI model to learn and predict with precision. Thus, it offers significant advancements in AI applications to medical imaging, particularly for PTB diagnosis and management, and provides a reliable and clinically applicable tool.

Furthermore, we established a 1-month gray zone following a change in the sputum test results of the patient for labeling purposes. This was necessary to enhance the certainty of the infection status of the patient after the sputum test result became negative because a negative sputum test result does not immediately indicate that the patient is no longer infected. We excluded CXR images obtained during this period to enhance the accuracy for determining when the likelihood of the contagion decreased to aid in patient treatment and management. This 1-month period was selected based on its relevance compared with 2, 4, 6, and 8 weeks. However, it is important to recognize that using this gray zone could potentially reduce the discriminative power of the model owing to the overlap of continuous and minor changes in images throughout the treatment process. Although extending the gray zone could improve the discrimination power, we elected not to extend it beyond 1 month, which is the minimum interval for follow-ups to assess the infectivity reduction during treatment.

The results of the decision curve analysis demonstrated that the AI model maintained the highest net benefit across most threshold probabilities. This indicates that the AI model is effective in accurately identifying patients with infectious PTB while reducing unnecessary treatments. These findings suggest that the model can significantly contribute to public health efforts in reducing the spread of PTB.

According to the subgroup analysis comparing the model evaluation for the COPD and non-COPD groups, the sensitivity of the model was lower in patients with COPD than in the non-COPD group. This may be because COPD has different pathological features in CXR, including various degrees of inflammation of the lungs, hyperinflation and emphysema, and consolidations and bronchiolitis, which are causes of acute exacerbation episodes [39]. When these features are overlaid with typical CXR findings of PTB infectivity, it may be difficult for AI models to evaluate PTB infectivity, which means that the presence of COPD may disrupt markers of PTB infectivity [40,41], leading to reduced sensitivity.

In the comparison of the all cancers and noncancer groups, we found that the model’s evaluation for the noncancer group was lower than that for all cancers for most metrics, except for specificity and NPV. This is because most patients with cancer had been treated with, or were receiving, chemotherapeutic agents. These treatments may impair their cellular and humoral immune systems. Moreover, lung cancer could adversely affect the immune response to PTB [42,43], potentially exacerbating PTB [44], which leads to pronounced changes on CXR [45]. Thus, the AI model may have been better equipped to evaluate signs of infectivity in the CXR images of these patients. In addition, active tuberculosis can increase the risk of cancer, and a cancer diagnosis can also heighten the risk of tuberculosis owing to a mutual interaction [46]. Consequently, the higher sensitivity of AI in detecting PTB in patients with cancer could assist in the screening of tuberculosis in these patients.

Although the proposed AI model exhibited notable performance, it has certain limitations. First, although interpretation techniques aid in understanding the decisions of the AI model and provide valuable insights, their interpretations can be complex. While these methods effectively highlight regions significant to the AI decision-making process, such as the right and left upper lobes commonly affected by PTB, they sometimes focus on areas that pose challenges for straightforward clinical correlation. This indicates that although the areas of focus of the proposed AI model were aligned with known PTB-affected regions, these techniques underscore the need for further research to elucidate specific infectivity biomarkers in images. In addition, in some CXR images, the model focused on regions with foreign objects, such as sternotomy suture material, Electrocardiogram leads, and pacemakers, interpreting them as positive cases. These objects may act as confounders. Figure 6 depicts some of these CXRs.

Second, although a 1-month gray period was used to evaluate changes in the infection status, it remains uncertain whether this timeframe is universally applicable to all patients or offers adequate flexibility for diverse scenarios.

Third, although this study emphasized the PTB diagnosis accuracy of the model, acknowledging the inherent constraints of retrospective research, it did not assess the long-term effects on patient outcomes following the application of the AI tool, indicating the necessity for future prospective studies.

Fourth, it included only patients who were successfully treated through a 6-month short-course treatment regimen; this methodology omits a range of treatment outcomes, including delayed response, treatment failure, and multidrug-resistant tuberculosis, and may therefore fail to encompass all phases of the tuberculosis treatment continuum.

Finally, the model may have a bias toward Koreans as the study patients were only Korean. Further experiments with multicountry data, including different ethnicities, are required to address this.

In conclusion, this study represents a step forward in tackling the challenges associated with PTB diagnosis by introducing an AI tool that uses CXR images to assess PTB infectivity. The proposed AI model and interpretation techniques can evaluate PTB infectivity and elucidate its decision-making process, thereby aiding in decisions regarding patient discharge or quarantine.

Therefore, the proposed approach aims to minimize the spread of PTB by identifying individuals who pose a risk of infection. Importantly, it offers a more accurate and expedited screening process than conventional smear tests and provides quicker results than culture methods, with the potential to significantly enhance the efficiency and effectiveness of PTB diagnosis and management.