Automatic Detection for Acromegaly Using Hand Photographs: A Deep-Learning Approach

Duan, Chuanwei; Chen, Wenli; Yao, Shun; Mao, Zhigang; Wang, Zongming; Hu, Bin; Jiao, Haosen; Zhu, Danyan; Zhu, Yiping; Wang, Haijun

doi:10.1109/access.2020.3046878

Cited by 5 publications

(10 citation statements)

References 31 publications

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“…The CAPA database [July 1998–December 2018; ( https://capa.wohenok.com/ )] is a patient-advocacy and non-profit organization, aiming to facilitate the recognition of acromegaly for patients and their families in mainland China. Demographics, clinical characteristics, radiographic features, hands photographs, and treatment approaches and outcomes have been described and reported by members in the Chinese Pituitary Adenoma Collaborative Group ( 12 , 19 , 32 , 35 ). Photographs of hands were taken at 3 months postoperatively by members of our research team (Mengqi Wang, Chengbin Duan, and Wenli Chen) at the outpatient department.…”

Section: Methodsmentioning

confidence: 99%

“…As a rare disease, patients are usually diagnosed with acromegaly in a special pituitary surgery center or neuroendocrinological department, and many of them, particularly those in the developing countries, usually come from remote areas, resulting in a high rate of loss to follow-up ( 12 , 15 ). With the emerging development of artificial intelligence (AI), some algorithms have demonstrated a potential application in auto-segmentation ( 16 , 17 ), early preoperative diagnosis ( 18 , 19 ), subtype classification ( 20 - 22 ), personalized treatment ( 23 ), assessment of response to treatments ( 24 - 26 ), outcome prediction ( 27 - 29 ), and other decision-making roles in the clinical management of patients with pituitary or other brain tumors ( 30 ). Theoretically, an ideal AI model should be trained and generated using a wealth of data, however, this is a major challenge in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

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Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach

Wang¹,

Duan²,

Yao³

et al. 2023

Quant Imaging Med Surg

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Background The primary treatment goals in acromegaly patients are complete surgical removal of underlying pituitary tumors and biochemical remission. One of the challenges in developing countries is the difficulty in monitoring postoperative biochemical levels in acromegaly patients, particularly those who live in remote areas or regions with limited medical resources. Methods In an attempt to overcome the abovementioned challenges, we conducted a retrospective study and established a mobile and low-cost method to predict biochemical remission in acromegaly patients after surgery, the efficacy of which was assessed retrospectively using the China Acromegaly Patient Association (CAPA) database. A total of 368 surgical patients from the CAPA database were successfully followed up to obtain their hand photographs. Demographics, baseline clinical characteristics, pituitary tumor features, and treatment details were collated. Postoperative outcome, defined as biochemical remission at the last follow-up timepoint, was assessed. Transfer learning with a new mobile tailored neurocomputing architecture, MobileNetv2, was used to explore the identical features that could be used as predictors of long-term biochemical remission after surgery. Results As expected, the MobileNetv2-based transfer learning algorithm was shown to predict biochemical remission with statistical accuracies of 0.96 and 0.76 in the training cohort (n=803) and validation cohort (n=200), respectively, and the loss function value was 0.82. Conclusions Our findings demonstrate the potential of the MobileNetv2-based transfer learning algorithm in predicting biochemical remission for postoperative patients who are at home or live far away from a pituitary or neuroendocrinological treatment center.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach

Wang¹,

Duan²,

Yao³

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…Reference Aim of the study [2] Detection [16] Detection and severity classification1-2-3 [17] Detection and measure of facial parameters [18] Self-screening from hand photographs…”

Section: Table 1 Aim Of Different Studies Related To Acromegaly Detec...mentioning

confidence: 99%

“…The model was tested on 124 participants, 62 acromegaly patients and they matched 62 normal people with same age and sex. In another perspective, [18] used hand photographs of 192 normal people and 635 acromegaly patients to train a Dynamic CNN based on RESNET34. The testing dataset contained 50 normal hands and 115 acromegaly patients' hands.…”

Section: Table 1 Aim Of Different Studies Related To Acromegaly Detec...mentioning

confidence: 99%

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The use of artificial intelligence for early detection of acromegaly from patients’ photographs

Rafi¹,

Elmghari²,

Elansari³

et al. 2022

World J. Adv. Res. Rev.

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Acromegaly is a rare endocrine disease. It manifests with a metabolic disorder and typical physical changes. It evolves gradually and the first obvious signs appears between 7 to 10 years. The effects on patients are both physical and psychological. The diagnostic delay is associated with increased mortality and disability and the drastic changes, in many cases, are irreversible. Early diagnosis will help patients with acromegaly live better lives and reduce the financial burden of their condition. We aim by this survey to investigate the ways artificial intelligence where used for the early detection of acromegaly from photographs. No literature survey was conducted, to our knowledge, that tackle the different artificial intelligence techniques used for this purpose, their results and limitations.

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Early Detection of Acromegaly Using a Novel Convolutional Neural Network

Leekkala,

Nakarmi

2022 ASEE Midwest Section Conference Proceedings

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Acromegaly occurs when the pituitary gland produces too much somatropin, causing the liver to release excessive amounts of IGF-1, leading to the abnormal growth of the hands, feet, and face. Acromegaly is difficult to diagnose and can lead to serious, sometimes even life-threatening, health problems such as Type II diabetes and heart disease. The early detection of Acromegaly reduces potential health complications and the risk of death. Deep Learning assisted early detection has now been proven feasible according to latest research and the prevalent success of Transfer Learning provides a potential path of non-computationally intensive detection. In this study, a dataset containing roughly 20 images were used to train a Convolutional Neural Network with Transfer learning that utilized ResNet-18 to mitigate the low dataset size. Firstly, Acromegaly and Non-Acromegaly images were placed into separate datasets and were further separated in a 70/30 training-validation split. This was run through the model, achieving a 65.62% validation accuracy over 25 epochs. This was paired with high training-validation loss values, 0.7/1.5 respectively, past epoch 25. To improve these losses, pairs of the same person were used to mitigate data imbalance within the datasets occurring from multiple same patient images. The datasets were comprised of Acromegaly/Non-Acromegaly (A/N) pairs and Non-Acromegaly/Non-Acromegaly (N/N) pairs and each pair was fed through a custom data loader to then be fed through two Resnet-18 models, which were able to train on the differences between normal (N/N) and abnormal (A/N) growth. This led to a 9.9% validation increase as well as training/validation loss values of (0.6/0.65) by epoch 25. This proved that non-computationally intensive detection of Acromegaly was possible with limited data, and a lightweight model could be distributed and used to assist doctors/researchers on whether a patient needs to test for Acromegaly or not, saving lives.

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Automatic Detection for Acromegaly Using Hand Photographs: A Deep-Learning Approach

Cited by 5 publications

References 31 publications

Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach

Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach

The use of artificial intelligence for early detection of acromegaly from patients’ photographs

Early Detection of Acromegaly Using a Novel Convolutional Neural Network

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