Development of a code-free machine learning model for the classification of cataract surgery phases

Touma, Samir; Antaki, Fares; Duval, Renaud

doi:10.1038/s41598-022-06127-5

Cited by 18 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the multivariate regression showed the worst performance of the three models compared in their study. Touma et al have even developed a completely code-free AutoML model with very high accuracy for classifying cataract surgery phases from videos 18 . These and many other studies demonstrate the great potential that already exists with current AutoML algorithms.…”

Section: Discussionmentioning

confidence: 99%

Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology

Musigmann

Akkurt

Krähling

et al. 2022

Sci Rep

View full text Add to dashboard Cite

To investigate the applicability and performance of automated machine learning (AutoML) for potential applications in diagnostic neuroradiology. In the medical sector, there is a rapidly growing demand for machine learning methods, but only a limited number of corresponding experts. The comparatively simple handling of AutoML should enable even non-experts to develop adequate machine learning models with manageable effort. We aim to investigate the feasibility as well as the advantages and disadvantages of developing AutoML models compared to developing conventional machine learning models. We discuss the results in relation to a concrete example of a medical prediction application. In this retrospective IRB-approved study, a cohort of 107 patients who underwent gross total meningioma resection and a second cohort of 31 patients who underwent subtotal resection were included. Image segmentation of the contrast enhancing parts of the tumor was performed semi-automatically using the open-source software platform 3D Slicer. A total of 107 radiomic features were extracted by hand-delineated regions of interest from the pre-treatment MRI images of each patient. Within the AutoML approach, 20 different machine learning algorithms were trained and tested simultaneously. For comparison, a neural network and different conventional machine learning algorithms were trained and tested. With respect to the exemplary medical prediction application used in this study to evaluate the performance of Auto ML, namely the pre-treatment prediction of the achievable resection status of meningioma, AutoML achieved remarkable performance nearly equivalent to that of a feed-forward neural network with a single hidden layer. However, in the clinical case study considered here, logistic regression outperformed the AutoML algorithm. Using independent test data, we observed the following classification results (AutoML/neural network/logistic regression): mean area under the curve = 0.849/0.879/0.900, mean accuracy = 0.821/0.839/0.881, mean kappa = 0.465/0.491/0.644, mean sensitivity = 0.578/0.577/0.692 and mean specificity = 0.891/0.914/0.936. The results obtained with AutoML are therefore very promising. However, the AutoML models in our study did not yet show the corresponding performance of the best models obtained with conventional machine learning methods. While AutoML may facilitate and simplify the task of training and testing machine learning algorithms as applied in the field of neuroradiology and medical imaging, a considerable amount of expert knowledge may still be needed to develop models with the highest possible discriminatory power for diagnostic neuroradiology.

show abstract

Section: Discussionmentioning

confidence: 99%

Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology

Musigmann

Akkurt

Krähling

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…13 A number of automated, publicly available ML platforms enable domain experts, including health care professionals, without coding expertise to train their own high-performing DL models. [14][15][16][17][18][19] Our aforementioned work demonstrates high performance and the ability to reproduce models demonstrating signals such as sex prediction from retinal fundus photographs. 20,21 In this article, we build on that work by using automated ML and constraining the availability of other necessary resources, including private data sets and costly expert labels, to determine if underresourced clinicians can design well-performing models.…”

mentioning

confidence: 91%

“…This framework enables ML model building without coding by largely automating the ML pipeline, including data set management, neural architecture search, and hyperparameter tuning . A number of automated, publicly available ML platforms enable domain experts, including health care professionals, without coding expertise to train their own high-performing DL models . Our aforementioned work demonstrates high performance and the ability to reproduce models demonstrating signals such as sex prediction from retinal fundus photographs .…”

Section: Introductionmentioning

confidence: 99%

Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral

Korot,

Gonçalves,

Huemer

et al. 2023

JAMA Ophthalmol

View full text Add to dashboard Cite

ImportanceDemocratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets.ObjectiveTo determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models.Design, Setting, and ParticipantsThis diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021. A self-training method without coding was used on 2 public data sets with retinal images from patients in France (Messidor-2 [n = 1748]) and the UK and US (EyePACS [n = 58 689]) and externally validated on 1 data set with retinal images from patients of a private Egyptian medical retina clinic (Egypt [n = 210]). An AI model was trained to classify referable diabetic retinopathy as an exemplar use case. Messidor-2 images were assigned adjudicated labels available on Kaggle; 4 images were deemed ungradable and excluded, leaving 1744 images. A total of 300 images randomly selected from the EyePACS data set were independently relabeled by 3 blinded retina specialists using the International Classification of Diabetic Retinopathy protocol for diabetic retinopathy grade and diabetic macular edema presence; 19 images were deemed ungradable, leaving 281 images. Data analysis was performed from February 1 to February 28, 2021.ExposuresUsing public data sets, a teacher model was trained with labeled images using supervised learning. Next, the resulting predictions, termed pseudolabels, were used on an unlabeled public data set. Finally, a student model was trained with the existing labeled images and the additional pseudolabeled images.Main Outcomes and MeasuresThe analyzed metrics for the models included the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. The Fisher exact test was performed, and 2-tailed P values were calculated for failure case analysis.ResultsFor the internal validation data sets, AUROC values for performance ranged from 0.886 to 0.939 for the teacher model and from 0.916 to 0.951 for the student model. For external validation of automated ML model performance, AUROC values and accuracy were 0.964 and 93.3% for the teacher model, 0.950 and 96.7% for the student model, and 0.890 and 94.3% for the manually coded bespoke model, respectively.Conclusions and RelevanceThese findings suggest that self-training using automated ML is an effective method to increase both model performance and generalizability while decreasing the need for costly expert labeling. This approach advances the democratization of AI by enabling clinicians without coding expertise or access to large, well-labeled private data sets to develop their own AI models.

show abstract

“…Other metaheuristic optimizers include the Henry gas solubility optimizer, slime mold optimizer, whale optimization optimizer, particle swarm optimizer, genetic algorithm, grey wolf optimizer, Harris hawks optimizer, and standalone marine predators optimizer were compared to the results obtained by the proposed approach [28]. Compared to existing methods, the suggested methodology in [28] performed exceptionally well in terms of high detection accuracy and cheap computing cost. For picture segmentation of COVID-19 instances, a hybrid metaheuristic optimizing technique was applied, in which the marine predators' optimizer was combined with the moth-flame optimizer [29].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Classification from X-Ray Images: An Approach to Implement Federated Learning on Decentralized Dataset

Siddique¹,

Talha²,

Aamir³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are preferred in most cases, and more importantly, they are non-invasive, inexpensive, and provide a faster response time. Recent advances in Artificial Intelligence (AI), in combination with state-of-the-art methods, have allowed for the diagnosis of COVID-19 using chest x-rays. This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme. In order to build a progressive global COVID-19 classification model, two edge devices are employed to train the model on their respective localized dataset, and a 3layered custom Convolutional Neural Network (CNN) model is used in the process of training the model, which can be deployed from the server. These two edge devices then communicate their learned parameter and weight to the server, where it aggregates and updates the global model. The proposed model is trained using an image dataset that can be found on Kaggle. There are more than 13,000 X-ray images in Kaggle Database collection, from that collection 9000 images of Normal and COVID-19 positive images are used. Each edge node possesses a different number of images; edge node 1 has 3200 images, while edge node 2 has 5800. There is no association between the datasets of the various nodes that are included in the network. By doing it in this manner, each of the nodes will have access to a separate image collection that

show abstract

Development of a code-free machine learning model for the classification of cataract surgery phases

Cited by 18 publications

References 24 publications

Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology

Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology

Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral

COVID-19 Classification from X-Ray Images: An Approach to Implement Federated Learning on Decentralized Dataset

Contact Info

Product

Resources

About