|Year : 2022 | Volume
| Issue : 3 | Page : 137-143
Identifying malignant nodules on chest X-rays: A validation study of radiologist versus artificial intelligence diagnostic accuracy
Bassam Mahboub1, Manoj Tadepalli2, Tarun Raj2, Rajalakshmi Santhanakrishnan2, Mahmood Yaseen Hachim3, Usama Bastaki4, Rifat Hamoudi1, Ehsan Haider5, Abdullah Alabousi5
1 Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
2 Artificial Intelligence, Qure.ai, Mumbai, Maharashtra, India
3 Molecular Medicine, MBRU, Dubai, United Arab Emirates
4 Radiology, Dubai Health Authority, Dubai, United Arab Emirates
5 Radiology, McMaster University, Hamilton, ON, Canada
|Date of Submission||08-Mar-2022|
|Date of Decision||16-Apr-2022|
|Date of Acceptance||19-Apr-2022|
|Date of Web Publication||27-Jul-2022|
Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah
United Arab Emirates
Source of Support: None, Conflict of Interest: None
Background: Three and half million anonymous X-rays were gathered from 45 locations worldwide (in-hospital and outpatient settings). qXR was initially trained on this massive dataset. We used an independent dataset of 13,426 chest X-rays from radiologists’ reports. The test data set included 213,459 X-rays chosen at random from a pool of 3.5 million X-rays. The dataset (development) was developed using the remaining X-rays received from the remaining patients. Methods: qXR is a deep learning algorithm-enabled software that is used to study nodules and malignant nodules on X-rays. We observed moderate to a substantial agreement even when observations were made with normal X-rays. Results: qXR presented a high area under the curve (AUC) of 0.99 with a 95% confidence interval calculated with the Clopper–Pearson method. The specificity obtained with qXR was 0.90, and the sensitivity was 1 at the operating threshold. The sensitivity value of qXR in detecting nodules was 0.99, and the specificity ranged from 0.87 to 0.92, with AUC ranging between 0.98 and 0.99. The malignant nodules were detected with a sensitivity ranging from 0.95 to 1.00, specificity between 0.96 and 0.99, and AUC from 0.99 to 1. The sensitivity of radiologists 1 and 2 was between 0.74 and 0.76, with a specificity ranging from 0.98 to 0.99. In detecting the malignant nodules, specificity ranged between 0.98 and 0.99, and sensitivity fell between 0.88 and 0.94. Conclusion: Machine learning model can be used as a passive tool to find incidental cases of lung cancer or as a triaging tool, which accelerate the patient journey through standard care pipeline for lung cancer.
Keywords: Artificial intelligence, chest X-rays, convolutional neural network, deep learning, malignant nodules
|How to cite this article:|
Mahboub B, Tadepalli M, Raj T, Santhanakrishnan R, Hachim MY, Bastaki U, Hamoudi R, Haider E, Alabousi A. Identifying malignant nodules on chest X-rays: A validation study of radiologist versus artificial intelligence diagnostic accuracy. Adv Biomed Health Sci 2022;1:137-43
|How to cite this URL:|
Mahboub B, Tadepalli M, Raj T, Santhanakrishnan R, Hachim MY, Bastaki U, Hamoudi R, Haider E, Alabousi A. Identifying malignant nodules on chest X-rays: A validation study of radiologist versus artificial intelligence diagnostic accuracy. Adv Biomed Health Sci [serial online] 2022 [cited 2022 Aug 8];1:137-43. Available from: http://www.abhsjournal.net/text.asp?2022/1/3/137/352496
| Background|| |
Deep learning (DL) algorithms help us detect various lung abnormalities in chest X-rays . DL algorithms were used to detect diabetic retinopathy by using photographs of the retinal fundus . While investigating skin cancer, the fine-grained object categories were analyzed using deep convolutional neural networks (CNNs) . The availability of original and viable datasets representing various lung abnormalities is seldom available . DL-based algorithms outperformed experienced physicians in determining several thoracic diseases . Although larger datasets are available, most of the X-rays were certified normal, limiting us in feeding the machine tools with accurate training data. CheXpert is one of the largest chest radiograph datasets that can provide uncertainty labels and supply comparisons between expert judgments . It has been a difficult task to collect X-rays with less defined edges, irregular margins, and orders that appear circumscribed from reliable sources. In most lung cancer patients, solitary lung nodules can appear on the X-rays, which can be considered as prima facie finding of the possible lung cancer in at least 20% of cases . This is critical in scaling up the investigation of the possible lung abnormalities. Although several tools are in place, non-contrast computed tomography (CT) is still a reliable technique to identify pulmonary nodules . Automated detection of intracranial hemorrhage using head CT scans was attempted using DL algorithms . CheXNet was used to detect pneumonia from chest X-rays using DL . Stage I cancers can be diagnosed with chest X- rays in the initial screening process . In all, 44% of the central lung cancers and 98% of peripheral lung cancers can appear as nodules . These nodules might be associated with or without adenopathy. In the related studies, it was suggested that 65% of the lung cancers appeared as nodules . There are always instances that ignore nodules that would otherwise need to investigate the lung cancers. This difficulty or oversight can be minimized with the use of machine algorithms. Although not a sole tool can present accurate results, these algorithms can aid in investigating lung abnormalities. Several computer-aided diagnostic methods can be used in lung cancer to be investigated on the X-rays . Several studies evaluated DL algorithms to gauge nodules on the test dataset that contained X-rays with normal and abnormal lung conditions. qXR v2.0 was used to analyze chest X-rays of tuberculosis patients . qXR is a software application that can utilize DL algorithms to detect nodules on chest X-rays. This study evaluates the performance of qXR in detecting malignant nodules. In addition, some of the abnormalities such as pleural effusion, opacity, and consolidation were also investigated using this software. The model score was established for each X-ray, and the performance was evaluated accordingly. Through this study, training and validation of qXR to detect nodules on chest X-rays are explained.
| Materials and methods|| |
About 3.5 million anonymous X-rays were collected from 45 centers (in-hospital and outpatient settings) spread worldwide. qXR was trained with this huge dataset initially. An independent dataset comprising 13,426 chest X-rays based on the reports generated by radiologists was used. These X-rays were acquired in posterior–anterior (PA), anterior–posterior (AP), supine, or lateral views. The lateral chest X-rays were not available; we made all efforts to get them but could not. Although lateral X-rays can be included if they are available, it is very hard to find the corresponding lateral X-rays for most AP/PA X-rays taken in general practice, and even harder to find them in cases of suspected nodules. The biopsy report should be used as the final ground truth when available. Radiologist interpretation has been used due to the lack of biopsy reports in these cases. The diversity in the dataset is based on the quality of X-rays, resolution, and size distribution. The test data comprised 213,459 X-rays that were randomly selected from a pool of 3.5 million X-rays used. The development dataset was framed from the remaining X-rays collected from the remaining patients and was used to develop the algorithm. In all, 500 nodules were randomly selected from the nodule-positive subset and 500 from the nodule-negative subset (determined by the original radiologist report). No signs or symptoms or clinical data were considered when deciding the inclusion/exclusion of a case from the study.
Validation and training datasets were extracted from these X-rays. In this study, chest X-rays from patients younger than 15 years and those exposed in the lateral view were excluded from test and development datasets. The development dataset and test dataset were treated as independent in the development of the algorithm. The presence/absence of the nodules as reported by radiologists was extracted using natural language processing techniques . At least one nodule was present in the test data comprising 10,200 X-rays, which was confirmed by radiologists. Randomized selections were made to choose 894 scans. From this collection, X-rays that exhibited nodules were classified as positive test sets, and those devoid of abnormalities and nodules were classified as negative test sets. Ground truth was established based on the annotated reports of three radiologists.
The radiologists classified these scans based on the presence/absence of nodules and if they were malignant or benign. The ground truth was confirmed based on the consensus of the opinions among radiologists. The performance of qXR with ground truth was compared with the reads of two radiologists. The accuracy of qXR was evaluated using positive predictive value (PPV), negative predictive value (NPV), specificity, sensitivity, and area under the curve (AUC) . Cohen’s kappa was used to assess the variability between a pair of radiologists . The consistency of the agreement in the radiologist’s group was tested using Fleiss’ kappa . The inter-rated agreement was used to determine the accuracy in interpreting chest X-rays of tuberculosis patients  and pneumonia patients .
In the primary stage of this research, three radiologists with 10 years of experience were recruited. Their respective identities were masked to ensure independent and unbiased reporting of the results. The radiological and clinical information of the anonymous patients was intentionally denied to the radiologists. Each radiologist was given the yes/no option to mark the X-rays for the presence of nodules. If nodules were present, the radiologists were asked to mark X-rays as either malignant or benign. Knowledge about histopathology of the nodules was not studied, and this study purely relied on the radiologists’ opinions. Radiologists employed their unbiased judgment based on the internal characteristics, calcification, contour, margin, and size distribution. In the presence of multiple nodules, and if at least one was malignant, the radiologists read it as malignant . In case of discrepancy raised with reports, the majority of the three reports were classified as ground truth. The selection of the cases was entirely made using the radiological report and was blinded to the clinical data/symptoms of the patient.
DL was used to train the CNNs to detect nodules on the X-rays . Specific architectures that constitute the basic blocks of this system are versions of residual networks aided with squeeze–excitation modules . We used modified vanilla versions of these architectures to process information with appropriate resolution. The quality, resolution, size distribution, and diversity were observed in the selected development dataset before feeding it into CNNs. X-rays were subjected to downsampling and image normalization to minimize source-dependent variation. The classification networks that built nodule detection systems were pre-trained to demarcate chest X-rays from others . Super-set was utilized in this process, and it constituted all the X-rays. The network output score ranged between 0 and 1, which indicated nodule occurrence . A pixel map was produced specifying the location of the nodule . An algorithm was developed to automatically detect malignancy using nodules on X-rays . To train the algorithm, 3000 patches isolated from nodule-bearing X-rays were used. Every patch was extracted by manually demarcating the nodule with a bounding box and resized appropriately. Radiologists gave labels stating malignant/benign nodules. Each chest X-ray was passed through a nodule detecting algorithm. A pixel map was produced according to a specific lung area. The obtained pixel map was cropped, and the resultant patch could pass onto the malignancy detection algorithm. This algorithm assigned a malignancy score to every patch. Each X-ray got a malignancy score after aggregating the scores of all the nodules present. Multiple networks of nodule and malignancy detection algorithms were trained in this process. These networks were classified based on the dataset (training) distribution, model initialization conditions, and type of architecture. A major ensembling scheme and a subset of these models were selected based on heuristics. This subset was used to integrate prediction and to produce final decisions utilizing each algorithm. The schema of datasets and algorithm training is represented in [Figure 1].
qXR was evaluated opposite ground truth of detecting nodules and even malignant nodules by utilizing area under the receiver operating characteristic (ROC) curve at 95% confidence interval (CI) . We have used PPV, NPV, specificity, and sensitivity in this process. Beta distribution was examined using the Clopper–Pearson method to calculate CI at 95% . This analysis was done using Python with packages such as NumPy, Pandas, PyMongo, and scikit-learn . The data used in this study were stored in the Mongo database .
| Results|| |
It was observed that 47% of the X-rays (patients of age greater than 30 years), 75% of X-rays have gender information, of which 58% of them are taken from male patients. In all, 14% of the X-rays reflected malignant nodules, whereas 45% exhibited nodules. The clinical characteristics of the test set are shown in [Table 1].
qXR detected nodules with a high AUC of 0.99 with a 95% CI. The specificity obtained was 0.90 with a sensitivity of 1 during the operating point [Table 2]. Agreement among the radiologists was moderate to substantial in the detection of nodules. Cohen’s κ value was used to describe the agreement between a pair of radiologists, and Fliess’ κ was used to know agreement among all radiologists.
|Table 2: Performance of qXR and radiologists in detecting nodules and malignant nodules versus ground truth.|
Click here to view
For nodules, Cohen’s κ value was 0.37–0.66 with Fliess’ κ value of 0.49. For malignant nodules, Cohen’s κ value was 0.59–0.77, with Fliess’ κ value of 0.67 [Table 3]. There was a slight agreement observed in the case of other abnormalities and non-nodular opacities. We observed a moderate to substantial agreement for even observations made with normal X-rays. The ROC curve of qXR versus radiologists is presented in [Figure 2] and [Figure 3], respectively. The AUC of malignant nodules was 1, and for nodules, it was 0.99. Owing to the appropriate AUC, we stratified the test set accordingly using the gender and age of the patients at the operating point. Gender was classified as men, women, and other classes, and age was stratified as ≤30, >30–≤60, and >60. We analyzed NPV, PPV, specificity, sensitivity, and accuracy. The performance of qXR remained the same when tested across all the subgroups [Table 4] and [Table 5].
|Table 3: Inter-rater agreement between three independent radiologists (I, II, and III).|
Click here to view
|Table 4: Performance of qXR in detecting nodules stratified by age and gender.|
Click here to view
|Table 5: Performance of qXR in detecting malignant nodules stratified by age and gender.|
Click here to view
The sensitivity value of qXR in detecting nodules was 0.99, and the specificity ranged from 0.87 to 0.92, with AUC ranging between 0.98 and 0.99. The malignant nodules were detected with a sensitivity ranging from 0.95 to 1.00, specificity between 0.96 and 0.99, and AUC from 0.99 to 1. The sensitivity of radiologists 1 and 2 was between 0.74 and 0.76, with a specificity ranging from 0.98 to 0.99. In detecting the malignant nodules, specificity ranged between 0.98 and 0.99, and sensitivity fell between 0.88 and 0.94.
| Discussion|| |
qXR is a robust model trained with 3.5 million chest X-rays, and it is being used to screen tuberculosis with high accuracy. This study used the expertise of three radiologists to establish ground truth. Compared with the ground truth, qXR exhibited high accuracy in detecting nodules and even labeled malignant nodules in the lungs. This accuracy was estimated utilizing specificity, sensitivity, and AUC of qXR in the detection of nodules. This model succeeded in detecting malignant nodules in all the subgroups of gender and age. Nodules larger than 2 cm can be translated as malignant lung cancer with 75% accuracy. Moreover, several artificial intelligence (AI) algorithms can process X-rays images in seconds enabling researchers to perform large-scale screening efficiently. qXR is an AI model used to screen patients with coronavirus, tuberculosis, and several lung diseases. Sim et al. studied 150 normal X-rays and 450 abnormal X-rays (cancerous as defined by CT and pathology) . CNN model read these scans. Although the addition of CNN to the radiologist reports enhanced the instrument’s sensitivity to detect malignant nodules, it did not outperform the judgment of only radiologists. In their study, the radiologists’ sensitivity was 0.54–0.84 and carried a false positive rate between 0.1 and 0.3. Finally, Nam et al. examined nodules using a large set of normal and abnormal X-rays with deep learning-based automatic detection (DLAD) algorithm with an AUC of 0.92–0.99 . qXR succeeded in attaining the same level of performance as DLAD. qXR can detect calcification of nodules; the final malignancy score assigned by qXR to each nodule depends on the calcification status of that nodule and is inversely proportional (i.e., the more calcified the nodule, the less score qXR assigns for malignancy).
We compared the reports generated from three radiologists with reports generated from qXR. In the detection of malignant nodules, radiologists and qXR exhibited similar sensitivity and specificity. In the detection of nodules, qXR presented high sensitivity than radiologists. This study hypothesizes that the high performance exhibited by qXR can be attributed to the size of the nodules as large-sized nodules reflect malignancy. Nodule size can define the solitary pulmonary nodule present in the chest CT scan, that is, <3 cm diameter (if greater than 3 cm, it can be considered mass). The nodules were marked as malignant nodules by the qXR and radiologists due to their larger size. Cautious interpretation is necessary in this case as slight size variation among nodules can slip the judgment and result in false positives/false negatives. In detecting malignant nodules, low sensitivity was observed due to their small size, which remained the same even when the CNN model was used. This study further hypothesized that calcification, contour, and margins could affect the sensitivity. Calcifications can be observed using CT, and low-kilovolt radiography can allow us to see calcifications present inside the nodules. All these parameters might have affected the judgment of the radiologists. We assume that mass size might have affected the low agreement established while interpreting nodules compared to malignant nodules.
There was no clinical context of patients supplied before the radiologists to establish ground truth. Providing clinical history might have affected the reports given by radiologists resulting in the biased ground truth. The room for error was significantly lowered by establishing ground truth, considering the majority of the opinions offered by the radiologists. There was no histology reports or follow-up CT scan to confirm malignancy. At least one-fourth of lung cancer cases can be diagnosed by radiography. Although chest X-rays offer low sensitivity, nodules detected can be investigated, and malignancy was confirmed in most cases. Guidelines recommend not to consider chest X-rays alone to screen for malignancy in the lungs. AI algorithms fed with X-ray images can provide results in few seconds, and hence, there is a possibility of low-cost screening within a limited time frame.
| Conclusion|| |
We propose using the machine learning model either as a passive tool, monitoring all the X-rays processed at an institution, thereby helping to find incidental cases of lung cancer, or as a triaging tool, which helps accelerate the patient journey through the standard care pipeline for lung cancer. The machine learning model can be embedded in its totality, wherein it takes the complete digital imaging and communications in medicine as the input and produces a lung malignancy risk score, which can be made use of as is appropriate in the given setting. This study used qXR (AI algorithm) to detect the nodules and malignant nodules in the chest X-rays with high specificity, sensitivity, and accuracy. Advanced studies can be commissioned to validate this model by the addition of clinical parameters. These studies can also reduce costs and reduce the time needed to determine lung cancer patients’ malignancy.
Owing to its accuracy, this algorithm can be integrated with other machine learning tools to enable doctors to arrive at appropriate judgments. Furthermore, this technology can be simplified and supplied to countries with a limited technical workforce, saving patients.
The authors would like to thank the management of Qure.ai for providing funds and technical infrastructure for this study.
All authors had significant and equal contributions to the conceptualization, data collection and analysis and the write up of the first and final draft. All authors are responsible for the scientific integrity of the manuscript.
This study was accepted by the ethical committee and review board of the institution where this research is done.
Declaration of patient consent
The patients were informed and they accepted to participate in this research with no restrictions.
Financial support and sponsorship
This work is funded by Qure.ai.
Conflict of interest
There are no conflicts of interest.
Data availability statement
The data set used in the current study is available (tick the appropriate option and fill the information).
Name of the public domain resources—None.
Data availability within the article or its supplementary materials
Data will be shared upon request available on request from Dr. Bassam ([email protected]). Dataset can be made available after the embargo period due to commercial restrictions.
| References|| |
Tang YX, Tang YB, Peng Y, Yan K, Bagheri M, Redd BA, et al
. Automated abnormality classification of chest radiographs using deep convolutional neural networks. NPJ Digit Med 2020;3:70.
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al
. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402-10.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al
. Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;546:686.
Bustos A, Pertusa A, Salinas JM, de la Iglesia-Vayá M PadChest: A large chest x-ray image dataset with multi-label annotated reports. Med Image Anal 2020;66:101797.
Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, et al
; DLAD Development and Evaluation Group. Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open 2019;2:e191095.
Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al
. CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI Conference on Artificial Intelligence. 2019;33:590-7.
Erasmus JJ, Connolly JE, McAdams HP, Roggli VL Solitary pulmonary nodules: Part I. Morphologic evaluation for differentiation of benign and malignant lesions. Radiographics 2000;20:43-58.
Bhalla AS, Das A, Naranje P, Irodi A, Raj V, Goyal A Imaging protocols for CT chest: A recommendation. Indian J Radiol Imaging 2019;29:236-46.
Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, et al
. Deep learning algorithms for detection of critical findings in head CT scans: A retrospective study. Lancet 2018;392: 2388-96.
Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al
. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv:171105225 [cs, stat]; 25 Dec 2017. Available from: http://arxiv.org/abs/1711.05225
. [Last accessed on 9 Sep 2021].
Purandare NC, Rangarajan V Imaging of lung cancer: Implications on staging and management. Indian J Radiol Imaging 2015;25:109-20.
Larici AR, Farchione A, Franchi P, Ciliberto M, Cicchetti G, Calandriello L, et al
. Lung nodules: Size still matters. European Respiratory Review 2017;26;1-10. Available from: https://err.ersjournals.com/content/26/146/170025
Gould MK, Donington J, Lynch WR, Mazzone PJ, Midthun DE, Naidich DP, et al
. Evaluation of individuals with pulmonary nodules: When is it lung cancer? Chest 2013;143:e93S-120S.
El-Baz A, Beache GM, Gimel’farb G, Suzuki K, Okada K, Elnakib A, et al
. Computer-aided diagnosis systems for lung cancer: Challenges and methodologies. Int J Biomed Imaging 2013;2013:942353.
Twabi HH, Semphere R, Mukoka M, Chiume L, Nzawa R, Feasey HRA, et al
. Pattern of abnormalities amongst chest X-rays of adults undergoing computer-assisted digital chest X-ray screening for tuberculosis in Peri-Urban Blantyre, Malawi: A cross-sectional study. Trop Med Int Health 2021;26:1427-37.
Pons E, Braun LM, Hunink MG, Kors JA Natural language processing in radiology: A systematic review. Radiology 2016;279:329-43.
Bradley SH, Hatton NLF, Aslam R, Bhartia B, Callister ME, Kennedy MP, et al
. Estimating lung cancer risk from chest X-ray and symptoms: A prospective cohort study. Br J Gen Pract 2021;71:e280-6.
Luo L, Luo X, Chen W, Liang C, Yao S, Huang W, et al
. Consistency analysis of programmed death-ligand 1 expression between primary and metastatic non-small cell lung cancer: A retrospective study. J Cancer 2020;11:974-82.
Endo C, Nakashima R, Taguchi A, Yahata K, Kawahara E, Shimagaki N, et al
. Inter-rater agreement of sputum cytology for lung cancer screening in Japan. Diagn Cytopathol 2015;43:545-50.
Sakurada S, Hang NT, Ishizuka N, Toyota E, Hung Le D, Chuc PT, et al
. Inter-rater agreement in the assessment of abnormal chest X-ray findings for tuberculosis between two Asian countries. BMC Infect Dis 2012;12:31.
Hopstaken RM, Witbraad T, van Engelshoven JM, Dinant GJ Inter-observer variation in the interpretation of chest radiographs for pneumonia in community-acquired lower respiratory tract infections. Clin Radiol 2004;59:743-52.
Schultheiss M, Schmette P, Bodden J, Aichele J, Müller-Leisse C, Gassert FG, et al
. Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance. Sci Rep 2021;11:15857.
Schultheiss M, Schober SA, Lodde M, Bodden J, Aichele J, Müller-Leisse C, et al
. A robust convolutional neural network for lung nodule detection in the presence of foreign bodies. Sci Rep 2020;10:12987.
Hu J, Shen L, Albanie S, Sun G, Wu E Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 2020;42:2011-23.
Ibrahim DM, Elshennawy NM, Sarhan AM Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Comput Biol Med 2021;132:104348.
Farhat H, Sakr GE, Kilany R Deep learning applications in pulmonary medical imaging: Recent updates and insights on COVID-19. Mach Vis Appl 2020;31:53.
Hollings N, Shaw P Diagnostic imaging of lung cancer. Eur Respir J 2002;19:722-42.
Del Ciello A, Franchi P, Contegiacomo A, Cicchetti G, Bonomo L, Larici AR Missed lung cancer: When, where, and why? Diagn Interv Radiol 2017;23:118-26.
Nash M, Kadavigere R, Andrade J, Sukumar CA, Chawla K, Shenoy VP, et al
. Deep learning, computer-aided radiography reading for tuberculosis: A diagnostic accuracy study from a tertiary hospital in India. Sci Rep 2020;10:210.
Jabbour SK, Lee KH, Frost N, Breder V, Kowalski DM, Pollock T, et al
. Pembrolizumab plus concurrent chemoradiation therapy in patients with unresectable, locally advanced, stage III non–small cell lung cancer: The phase 2 KEYNOTE-799 nonrandomized trial. JAMA Oncol 2021;7:1351-9.
Mathur P Key technological advancements in healthcare. In: Mathur P, editor. Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance. Berkeley, California: Apress; 2019:13-35. Available from: https://doi.org/10.1007/978-1-4842-3787-8_2
. [Last accessed on 9 Sep 2021].
Ferreira Junior JR, Oliveira MC, de Azevedo-Marques PM Cloud-based NoSQL open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research. J Digit Imaging 2016;29:716-29.
Sim Y, Chung MJ, Kotter E, Yune S, Kim M, Do S, et al
. Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology 2020;294:199-209.
Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, et al
. Deep learning algorithms for detection of critical findings in head CT scans: A retrospective study. Lancet 2018;392: 2388-96.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]