AI in medical physics: guidelines for publication

Naqa, Issam El; Boone, John M.; Benedict, Stanley H; Goodsitt, Mitchell M.; Chan, Heang Ping; Drukker, Karen; Hadjiiski, Lubomir M.; Ruan, Dan; Sahiner, Berkman

doi:10.1002/mp.15170

Cited by 32 publications

(13 citation statements)

References 10 publications

(20 reference statements)

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“…Though several recommendations have emerged to improve the reporting of AI for biomedical applications [109][110][111][112][113] and imaging specifically, 114 little have been yet developed on the career transition process across radiology, medical imaging physics, and computer science. We recognize the great potential of imaging and AI in a variety of applications related to COVID-19 (such as those discussed in Sec.…”

Section: Discussionmentioning

confidence: 99%

Lessons learned in transitioning to AI in the medical imaging of COVID-19

Naqa

Fuhrman

et al. 2021

J. Med. Imag.

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. The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc across the world. It also created a need for the urgent development of efficacious predictive diagnostics, specifically, artificial intelligence (AI) methods applied to medical imaging. This has led to the convergence of experts from multiple disciplines to solve this global pandemic including clinicians, medical physicists, imaging scientists, computer scientists, and informatics experts to bring to bear the best of these fields for solving the challenges of the COVID-19 pandemic. However, such a convergence over a very brief period of time has had unintended consequences and created its own challenges. As part of Medical Imaging Data and Resource Center initiative, we discuss the lessons learned from career transitions across the three involved disciplines (radiology, medical imaging physics, and computer science) and draw recommendations based on these experiences by analyzing the challenges associated with each of the three associated transition types: (1) AI of non-imaging data to AI of medical imaging data, (2) medical imaging clinician to AI of medical imaging, and (3) AI of medical imaging to AI of COVID-19 imaging. The lessons learned from these career transitions and the diffusion of knowledge among them could be accomplished more effectively by recognizing their associated intricacies. These lessons learned in the transitioning to AI in the medical imaging of COVID-19 can inform and enhance future AI applications, making the whole of the transitions more than the sum of each discipline, for confronting an emergency like the COVID-19 pandemic or solving emerging problems in biomedicine.

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Section: Discussionmentioning

confidence: 99%

Lessons learned in transitioning to AI in the medical imaging of COVID-19

Naqa

Fuhrman

et al. 2021

J. Med. Imag.

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“…With the release of the HaN‐Seg dataset and deployment of the accompanying HaN‐Seg challenge, our aim is, therefore, to test the hypothesis that the accuracy and reliability of OAR segmentation can be improved by exploiting the fused information from both CT and MR images, with the objective to design, develop, and evaluate novel auto‐segmentation algorithms that rely on robust and accurate registration algorithms and can be benchmarked on a common dataset. Considering the recent growth in the application of AI, especially DL, 45 the devised HaN‐Seg dataset has also the potential to contribute to a more objective and trustful reporting of research outcomes. Nevertheless, future versions of the HaN‐Seg dataset may be enriched with additional CT/MR image pairs to capture a wider distribution of the anatomical variability among patients 10 or additional reference segmentations to evaluate the interobserver variability of manual annotation, 7 as well as with radiation dose distribution maps as defined by RT planning to evaluate the dosimetric impact of auto‐segmentation results 46 …”

Section: Discussionmentioning

confidence: 99%

HaN‐Seg: The head and neck organ‐at‐risk CT and MR segmentation dataset

et al. 2023

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For the cancer in the head and neck (HaN), radiotherapy (RT) represents an important treatment modality. Segmentation of organs-at-risk (OARs) is the starting point of RT planning,however,existing approaches are focused on either computed tomography (CT) or magnetic resonance (MR) images, while multimodal segmentation has not been thoroughly explored yet. We present a dataset of CT and MR images of the same patients with curated reference HaN OAR segmentations for an objective evaluation of segmentation methods. Acquisition and validation methods:The cohort consists of HaN images of 56 patients that underwent both CT and T1-weighted MR imaging for imageguided RT. For each patient, reference segmentations of up to 30 OARs were obtained by experts performing manual pixel-wise image annotation. By maintaining the distribution of patient age and gender, and annotation type, the patients were randomly split into training Set 1 (42 cases or 75%) and test Set 2 (14 cases or 25%). Baseline auto-segmentation results are also provided by training the publicly available deep nnU-Net architecture on Set 1, and evaluating its performance on Set 2. Data format and usage notes: The data are publicly available through an openaccess repository under the name HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Dataset. Images and reference segmentations are stored in the NRRD file format, where the OAR filenames correspond to the nomenclature recommended by the American Association of Physicists in Medicine,and OAR and demographics information is stored in separate commaseparated value files. Potential applications: The HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge is launched in parallel with the dataset release to promote the development of automated techniques for OAR segmentation in the HaN. Other potential applications include out-of -challenge algorithm development and benchmarking, as well as external validation of the developed algorithms.

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“…More tellingly, the top 5% of papers were cited 68−183 and 22−49 times for DL and MC papers, respectively. Because of the growing number of poor quality submissions, in 2020 we initiated development of machine‐ and deep‐learning paper review guidelines in collaboration with the current editorial team 16 Growing reliance on team science and proliferation of submissions authored by non‐medical physicists.…”

Section: Reflections and Lessons Learnedmentioning

confidence: 99%