Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules

Tandon, Yasmeen K.; Bartholmai, Brian J.; Koo, Chi Wan

doi:10.21037/jtd-2019-cptn-03

Cited by 31 publications

(24 citation statements)

References 87 publications

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“…Well known candidates are software packages that search for specific lesions in specific types of images, such as acquired in the frame of organized screening [38,39] but many questions must be answered before they gain wide spread use in organized breast cancer screening [40] and in other domains of medicine [41]. Other AI tools also have the potential to achieve global acceptance, in many domains of medicine [42][43][44]. Typical results are lower noise (reconstructed) images, segmentation of specific anatomical parts of interest, quantitative data of all (cancerous) lesions rather than subjective readings of only a selection, quantitative lesion follow up, new data or insights in new diseases, the comparison of quantitative image data to that of other patients or to asymptomatic groups to better situate the patient's disease or prognosis, and improved outcome in general.…”

Section: Discussionmentioning

confidence: 99%

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

Bosmans

Zanca²,

Gelaude

2021

Physica Medica

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In this study, we propose a framework to help the MPE take up a unique and important role at the introduction of AI solutions in clinical practice, and more in particular at procurement, acceptance, commissioning and QA. Material and methods: The steps for the introduction of Medical Radiological Equipment in a hospital setting were extrapolated to AI tools. Literature review and in-house experience was added to prepare similar, yet dedicated test methods. Results: Procurement starts from the clinical cases to be solved and is usually a complex process with many stakeholders and possibly many candidate AI solutions. Specific KPIs and metrics need to be defined. Acceptance testing follows, to verify the installation and test for critical exams. Commissioning should test the suitability of the AI tool for the intended use in the local institution. Results may be predicted from peer reviewed papers that treat representative populations. If not available, local data sets can be prepared to assess the KPIs, or 'virtual clinical trials' could be used to create large, simulated test data sets. Quality assurance must be performed periodically to verify if KPIs are stable, especially if the software is upscaled or upgraded, and as soon as selflearning AI tools would enter the medical practice. Discussion: MPEs are well placed to bridge between manufacturer and medical team and help from procurement up to reporting to the management board. More work is needed to establish consolidated test protocols.

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Section: Discussionmentioning

confidence: 99%

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

Bosmans

Zanca²,

Gelaude

2021

Physica Medica

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“…One also has to keep in mind that most algorithms are tested on datasets, which are very similar to the training datasets, a fact that may cause an overestimation of their performance; this phenomenon has been described in the literature as "overfitting" (33). Despite the promising results of AI-supported diagnostic modules, widespread implementation is still delayed; mostly owed to inadequate performance, lack of workflow integration and assessment tools (32,33).…”

Section: Discussionmentioning

confidence: 99%

Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists

Peters¹,

Decasper²,

Munz³

et al. 2021

J Thorac Dis

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Background: Despite the decreasing relevance of chest radiography in lung cancer screening, chest radiography is still frequently applied to assess for lung nodules. The aim of the current study was to determine the accuracy of a commercial AI based CAD system for the detection of artificial lung nodules on chest radiograph phantoms and compare the performance to radiologists in training. Methods: Sixty-one anthropomorphic lung phantoms were equipped with 140 randomly deployed artificial lung nodules (5, 8, 10, 12 mm). A random generator chose nodule size and distribution before a two-plane chest X-ray (CXR) of each phantom was performed. Seven blinded radiologists in training ( 2fellows, 5 residents) with 2 to 5 years of experience in chest imaging read the CXRs on a PACS-workstation independently. Results of the software were recorded separately. McNemar test was used to compare each radiologist's results to the AI-computer-aided-diagnostic (CAD) software in a per-nodule and a per-phantom approach and Fleiss-Kappa was applied for inter-rater and intra-observer agreements. Results: Five out of seven readers showed a significantly higher accuracy than the AI algorithm. The pooled accuracies of the radiologists in a nodule-based and a phantom-based approach were 0.59 and 0.82 respectively, whereas the AI-CAD showed accuracies of 0.47 and 0.67, respectively. Radiologists' average sensitivity for 10 and 12 mm nodules was 0.80 and dropped to 0.66 for 8 mm (P=0.04) and 0.14 for 5 mm nodules (P<0.001). The radiologists and the algorithm both demonstrated a significant higher sensitivity for peripheral compared to central nodules (0.66 vs. 0.48; P=0.004 and 0.64 vs. 0.094; P=0.025, respectively).Inter-rater agreements were moderate among the radiologists and between radiologists and AI-CAD software (K'=0.58±0.13 and 0.51±0.1). Intra-observer agreement was calculated for two readers and was almost perfect for the phantom-based (K'=0.85±0.05; K'=0.80±0.02); and substantial to almost perfect for the nodule-based approach (K'=0.83±0.02; K'=0.78±0.02). Conclusions: The AI based CAD system as a primary reader acts inferior to radiologists regarding lung nodule detection in chest phantoms. Chest radiography has reasonable accuracy in lung nodule detection if read by a radiologist alone and may be further optimized by an AI based CAD system as a second reader.

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“…However, detection of lung nodules is not as simple as it looks, as pulmonary nodules usually appear as a white spherical structure that could mimic a nearby small blood vessel or a collapsed bronchiole. In addition, the inter-reader variations in detection and the characterization of pulmonary nodules are merely subjective issues [ 10 , 78 , 79 ]. This opens the way for artificial intelligence and deep learning to overcome human errors and provide more effective procedures.…”

Section: Pulmonary Nodule Detection and Segmentationmentioning

confidence: 99%

How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules

Fahmy

Kandil

Khelifi

et al. 2022

Cancers

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Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification.

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Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules

Cited by 31 publications

References 87 publications

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists

How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules

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