Mohammad Arafat Hussain, PhD

selected publications

1. F. Neuroimaging

   Inferring Neurocognition Using Artificial Intelligence on Brain MRIs [In Press].

   Mohammad Arafat Hussain, Ellen Grant, and Yangming Ou

   Frontiers in Neuroimaging 2024

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   Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition and intelligence. A core mystery is the MRI explanation of individual differences in neurocognition and intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation need to be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on machine intelligence inferring human intelligence.

   @article{hussain2024inferring,
     doi = {https://doi.org/10.20944/preprints202302.0452.v1},
     journal = {Frontiers in Neuroimaging},
     title = {Inferring Neurocognition Using Artificial Intelligence on Brain MRIs [In Press].},
     author = {Hussain, Mohammad Arafat and Grant, Ellen and Ou, Yangming},
     year = {2024},
     publisher = {Frontiers in Neuroimaging}
   }

2. ASPNR

   The Role of Expert MRI Scores in Predicting Adverse 18-22-month Outcomes for Hypoxic Ischemic Encephalopathy in Neonatal Research Network Trials

   Ankush Kesri, Rina Bao, Chuan-Heng Hsiao, Rutvi Vyas, Mohammad Arafat Hussain, Scott McDonald, Jeanette Auman, Seetha Shankaran, Abbott Laptook, Michael Cotten, and 2 more authors

   In 7th Annual Meeting of American Society of Pediatric Neuroradiology 2025

   Bib PDF

   @inproceedings{Kesri2024therole,
     title = {The Role of Expert MRI Scores in Predicting Adverse 18-22-month Outcomes for Hypoxic Ischemic Encephalopathy in Neonatal Research Network Trials},
     author = {Kesri, Ankush and Bao, Rina and Hsiao, Chuan-Heng and Vyas, Rutvi and Hussain, Mohammad Arafat and McDonald, Scott and Auman, Jeanette and Shankaran, Seetha and Laptook, Abbott and Cotten, Michael and Grant, Ellen and Ou, Yangming},
     booktitle = {7th Annual Meeting of American Society of Pediatric Neuroradiology},
     year = {2025},
     organization = {ASPNR}
   }

3. Scientific Reports

   Deep Learning of Structural MRI Predicts Fluid, Crystallized, and General Intelligence [In Press]

   Mohammad Arafat Hussain, Danielle LaMay, Ellen Grant, and Yangming Ou

   Scientific Reports - Nature 2024

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   Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. Nevertheless, population-level association does not fully account for individual variability in intelligence. To address this, individual prediction studies emerge recently. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual subject’s verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, FSIQ), which reflect both fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input images, six deep learning models, and two outcome settings, on 850 autistic and healthy subjects 6-64 years of age. Results show promise with statistical significance, and also open up questions inviting further future studies.

   @article{hussain2024deep,
     doi = {https://doi.org/10.1038/s41598-024-78157-0},
     title = {Deep Learning of Structural MRI Predicts Fluid, Crystallized, and General Intelligence [In Press]},
     author = {Hussain, Mohammad Arafat and LaMay, Danielle and Grant, Ellen and Ou, Yangming},
     journal = {Scientific Reports - Nature},
     year = {2024},
     publisher = {Nature}
   }

4. PRIME

   RCT: Relational Connectivity Transformer for Enhanced Prediction of Absolute and Residual Intelligence

   Mohammad Arafat Hussain, Ellen Grant, and Yangming Ou

   In 7th International Workshop of Predictive Intelligence in Medicine (PRIME) 2024

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   This paper introduces the Relational Connectivity Transformer (RCT), a novel Graph-Transformer model designed for predicting absolute and residual full-scale intelligence quotient (FSIQ), performance IQ (PIQ), and verbal IQ (VIQ) scores from resting-state functional magnetic resonance imaging (rs-fMRI) data. Early prediction of neurocognitive impairments via IQ scores may allow for timely intervention. To this end, our RCT model leverages a relation-learning strategy from paired sample data via a novel graph-based transformer framework. Through a comprehensive comparison with state-of-the-art approaches in a 5-fold cross-validation setup, our model demonstrated superior performance. Statistical analysis confirmed the significant improvement (p < 0.05) in FSIQ prediction, strengthening the efficacy of the proposed method. This work marks the first application of a Graph-Transformer in predicting IQ scores using rs-fMRI, introducing a novel learning strategy and contributing to the ongoing efforts to enhance the accuracy and reliability of human intelligence predictions based on functional brain connectivity.

   @inproceedings{hussain2024rct,
     title = {RCT: Relational Connectivity Transformer for Enhanced Prediction of Absolute and Residual Intelligence},
     author = {Hussain, Mohammad Arafat and Grant, Ellen and Ou, Yangming},
     booktitle = {7th International Workshop of Predictive Intelligence in Medicine (PRIME)},
     year = {2024},
     organization = {Springer, Cham}
   }

5. arXiv

   Foundation AI Model for Medical Image Segmentation

   Rina Bao, Erfan Darzi, Sheng He, Chuan-Heng Hsiao, Mohammad Arafat Hussain, Jingpeng Li, Atle Bjornerud, Ellen Grant, and Yangming Ou

   2024

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   @article{bao2024foundationaimodelmedical,
     title = {Foundation AI Model for Medical Image Segmentation},
     author = {Bao, Rina and Darzi, Erfan and He, Sheng and Hsiao, Chuan-Heng and Hussain, Mohammad Arafat and Li, Jingpeng and Bjornerud, Atle and Grant, Ellen and Ou, Yangming},
     year = {2024},
   }

6. MEP

   Patient’s Airway Monitoring during Cardiopulmonary Resuscitation using Deep Networks

   Mahmoud Marhamati, Behnam Dorri, Shima Imannezhad, Mohammad Arafat Hussain, Ali Asghar Neshat, Abulfazl Kakmishi, and Mohammad Momeny

   Medical Engineering & Physics 2024

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   Cardiopulmonary resuscitation (CPR) is a crucial life-saving technique commonly administered to individuals experiencing cardiac arrest. Among the important aspects of CPR is ensuring the correct airway position of the patient, which is typically monitored by human tutors or supervisors. This study aims to utilize deep transfer learning for the detection of the patient’s correct and incorrect airway position during cardiopulmonary resuscitation. To address the challenge of identifying the airway position, we curated a dataset consisting of 198 recorded video sequences, each lasting 6-8 seconds, showcasing both correct and incorrect airway positions during mouth-to-mouth breathing and breathing with an Ambu Bag. We employed six cutting-edge deep networks, namely DarkNet19, EfficientNetB0, GoogleNet, MobileNet-v2, ResNet50, and NasnetMobile. These networks were initially pre-trained on computer vision data and subsequently fine-tuned using the CPR dataset. The validation of the fine-tuned networks in detecting the patient’s correct airway position during mouth-to-mouth breathing achieved impressive results, with the best sensitivity (98.8%), specificity (100%), and F-measure (97.2%). Similarly, the detection of the patient’s correct airway position during breathing with an Ambu Bag exhibited excellent performance, with the best sensitivity (100%), specificity (99.8%), and F-measure (99.7%).

   @article{marhamati2024patient,
     doi = {https://doi.org/10.1016/j.medengphy.2024.104179},
     title = {Patient's Airway Monitoring during Cardiopulmonary Resuscitation using Deep Networks},
     author = {Marhamati, Mahmoud and Dorri, Behnam and Imannezhad, Shima and Hussain, Mohammad Arafat and Neshat, Ali Asghar and Kakmishi, Abulfazl and Momeny, Mohammad},
     journal = {Medical Engineering \& Physics},
     pages = {104179},
     year = {2024},
     publisher = {Elsevier}
   }

7. bioRxiv

   Enhancing Neurocognitive Outcome Prediction in Congenital Heart Disease Patients: The Role of Brain Age Biomarkers and Beyond

   Mohammad Arafat Hussain, Ellen Grant, and Yangming Ou

   bioRxiv 2023

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   This paper aimed to investigate the predictive power of combining demographic, socioeconomic, and genetic factors with a brain MRI-based quantified measure of accelerated brain aging (referred to as deltaAGE) for neurocognitive outcomes in adolescents and young adults with Congenital Heart Disease (CHD). Our hypothesis posited that including the brain age biomarker (deltaAGE) would enhance neurocognitive outcome predictions compared to models excluding it. We conducted comprehensive analyses, including leave-one-subject-out and leave-one-group-out cross-validation techniques. Our results demonstrated that the inclusion of deltaAGE consistently improved prediction performance when considering the Pearson correlation coefficient, a preferable metric for this study. Notably, the deltaAGE-augmented models consistently outperformed those without deltaAGE across all cross-validation setups, and these correlations were statistically significant (p-value < 0.05). Therefore, our hypothesis that incorporating the brain-age biomarker alongside demographic, socioeconomic, and genetic factors enhances neurocognitive outcome predictions in adolescents and young adults with CHD is supported by the findings.

   @article{hussain2023enhancing,
     year = {2023},
     doi = {https://doi.org/10.1101/2023.09.01.555976},
     pages = {2023-02},
     title = {Enhancing Neurocognitive Outcome Prediction in Congenital Heart Disease Patients: The Role of Brain Age Biomarkers and Beyond},
     journal = {bioRxiv},
     author = {Hussain, Mohammad Arafat and Grant, Ellen and Ou, Yangming},
     publisher = {Cold Spring Harbor Laboratory}
   }

8. Displays

   LAIU-Net: A learning-to-augment incorporated robust U-Net for depressed humans’ tongue segmentation

   Mahmoud Marhamati, Ali Asghar Latifi Zadeh, Masoud Mojdehi Fard, Mohammad Arafat Hussain, Khalegh Jafarnezhad, Ahad Jafarnezhad, Mehdi Bakhtoor, and Mohammad Momeny

   Displays 2023

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   Computer-aided tongue diagnosis system requires segmentation of the tongue body. The frequent movement of the tongue due to its natural flexibility often causes shape variability in photographs across subjects, which makes segmenting the tongue challenging from non-tongue elements, such as the lips, teeth, and other objects in the background of the tongue. The flexibility of the tongue causes a further challenge in maintaining a similar shape and style when taking photos of many healthy subjects and patients. To address these challenges, we have built a tongue dataset, where the tongue of each subject has been scanned thrice with an interval of less than a second. We have collected 333 tongue images from 111 depressed humans, who have been diagnosed with depression by a psychiatrist. In addition, in this paper, we propose a learning-to-augment incorporated U-Net (LAIU-Net) for the segmentation of the depressed human tongue in photographic images. The best policies for data augmentation were automatically chosen with the proposed LAIU-Net. For this purpose, we corrupted photographic tongue images with the Gaussian, speckle, and Poisson noise. The proposed approach addresses the overfitting problem as well as increases the generalizability of a deep network. We have compared the performance of the proposed LAIU-Net with that of other state-of-the-art U-Net configurations. Our LAIU-Net approach achieved a mean boundary F1 score of 93.1%.

   @article{MARHAMATI2023102371,
     title = {LAIU-Net: A learning-to-augment incorporated robust U-Net for depressed humans’ tongue segmentation},
     journal = {Displays},
     pages = {102371},
     year = {2023},
     issn = {0141-9382},
     doi = {https://doi.org/10.1016/j.displa.2023.102371},
     author = {Marhamati, Mahmoud and Zadeh, Ali Asghar Latifi and Fard, Masoud Mojdehi and Hussain, Mohammad Arafat and Jafarnezhad, Khalegh and Jafarnezhad, Ahad and Bakhtoor, Mehdi and Momeny, Mohammad},
     keywords = {Tongue segmentation, learning-to-augment strategy, data augmentation, deep learning, U-Net},
   }

9. arXiv

   Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies [In review]

   Abrar Morshed, Abdulla Al Shihab, Md Abrar Jahin, Md Jaber Al Nahian, Md Murad Hossain Sarker, Md Sharjis Ibne Wadud, Other Authors, and Mohammad Arafat Hussain

   arXiv 2024

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   The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings to detect COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on ultrasound-based AI studies for COVID-19 detection that use public or private lung ultrasound datasets. We surveyed articles that used publicly available lung ultrasound datasets for COVID-19 and reviewed publicly available datasets and organize ultrasound-based AI studies per dataset. We analyzed and tabulated studies in several dimensions, such as data preprocessing, AI models, cross-validation, and evaluation criteria. In total, we reviewed 42 articles, where 28 articles used public datasets, and the rest used private data. Our findings suggest that ultrasound-based AI studies for the detection of COVID-19 have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

   @article{morshed2024ultrasound,
     doi = {https://doi.org/10.48550/arXiv.2411.05029},
     journal = {arXiv},
     title = {Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies [In review]},
     author = {Morshed, Abrar and Al Shihab, Abdulla and Jahin, Md Abrar and Al Nahian, Md Jaber and Sarker, Md Murad Hossain and Wadud, Md Sharjis Ibne and Authors, Other and Hussain, Mohammad Arafat},
     year = {2024},
     publisher = {arXiv}
   }

10. Nature Conf.

    Machine Intelligence to Predict Human Intelligence

    Mohammad Arafat Hussain, Danielle LaMay, Ellen Grant, and Yangming Ou

    In AI, neuroscience and hardware: From neural to artificial systems and back again 2022

    Bib PDF Poster

    @inproceedings{hussain2022machine,
      title = {Machine Intelligence to Predict Human Intelligence},
      author = {Hussain, Mohammad Arafat and LaMay, Danielle and Grant, Ellen and Ou, Yangming},
      booktitle = {AI, neuroscience and hardware: From neural to artificial systems and back again},
      year = {2022},
      organization = {Nature Conferences}
    }