A Visual Deep Learning Model to Localize Parathyroid-Specific Autofluorescence on Near-Infrared Imaging : Localization of Parathyroid Autofluorescence with Deep Learning

The use of artificial intelligence to detect parathyroid tissue on ex vivo specimens during thyroidectomy and parathyroidectomy procedures using near-infrared autofluorescence signals

BACKGROUND

In thyroidectomy and parathyroidectomy procedures, diagnostic dilemmas related to whether an index tissue is of parathyroid or nonparathyroid origin frequently arise. Current options of frozen section and parathyroid aspiration are time-consuming. Parathyroid glands appear brighter than surrounding tissues on near-infrared autofluorescence imaging. The aim of this study was to develop an artificial intelligence model differentiating parathyroid tissue on surgical specimens based on near-infrared autofluorescence.

METHODS

With institutional review board approval, an image library of ex vivo specimens obtained in thyroidectomy and parathyroidectomy procedures was created between November 2019 and April 2023 at a single academic center. Ex vivo autofluorescence images of surgically removed parathyroid glands, thyroid glands, lymph nodes, and thymic tissue were uploaded into an artificial intelligence platform. Two different models were trained, with the first model using autofluorescence images from all specimens, including thyroid, and the second model excluding thyroid, to prevent the effect of specimen size on the results. Deep-learning models were trained to detect autofluorescence signals specific to parathyroid glands. Randomly chosen 80% of data were used for training, 10% for validation, and 10% for testing. Recall, precision, and area under the curve of models were calculated.

RESULTS

Surgical procedures included 377 parathyroidectomies, 239 total thyroidectomies, 97 thyroid lobectomies, and 32 central neck dissections. For the development of the model, 1151 images from a total of 678 procedures were used. The dataset comprised 648 parathyroid, 379 thyroid, 104 lymph node, and 20 thymic tissue images. The overall precision, recall, and area under the curve of the model to detect parathyroid tissue were 96.5%, 96.5%, and 0.985, respectively. False negatives were related to dark and large parathyroid glands.

CONCLUSION

The visual deep-learning model developed to identify parathyroid tissue in ex vivo specimens during thyroidectomy and parathyroidectomy demonstrated a high sensitivity and positive predictive value. This suggests potential utility of near-infrared autofluorescence imaging to improve intraoperative efficiency by reducing the need for frozen sections and parathyroid hormone aspirations to confirm parathyroid tissue.

Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods

BACKGROUND

Postoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged during surgery. Although intraoperative images are rarely used to train artificial intelligence (AI) because of its complex nature, AI may be trained to intraoperatively detect parathyroid glands using various augmentation methods. The purpose of this study was to train an effective AI model to detect parathyroid glands during thyroidectomy.

METHODS

Video clips of the parathyroid gland were collected during thyroid lobectomy procedures. Confirmed parathyroid images were used to train three types of datasets according to augmentation status: baseline, geometric transformation, and generative adversarial network-based image inpainting. The primary outcome was the average precision of the performance of AI in detecting parathyroid glands.

RESULTS

152 Fine-needle aspiration-confirmed parathyroid gland images were acquired from 150 patients who underwent unilateral lobectomy. The average precision of the AI model in detecting parathyroid glands based on baseline data was 77%. This performance was enhanced by applying both geometric transformation and image inpainting augmentation methods, with the geometric transformation data augmentation dataset showing a higher average precision (79%) than the image inpainting model (78.6%). When this model was subjected to external validation using a completely different thyroidectomy approach, the image inpainting method was more effective (46%) than both the geometric transformation (37%) and baseline (33%) methods.

CONCLUSION

This AI model was found to be an effective and generalizable tool in the intraoperative identification of parathyroid glands during thyroidectomy, especially when aided by appropriate augmentation methods. Additional studies comparing model performance and surgeon identification, however, are needed to assess the true clinical relevance of this AI model.

An automatic parathyroid recognition and segmentation model based on deep learning of near-infrared autofluorescence imaging

INTRODUCTION

Near-infrared autofluorescence imaging (NIFI) can be used to identify parathyroid gland (PG) during surgery. The purpose of the study is to establish a new model, help surgeons better identify, and protect PGs.

METHODS

Five hundred and twenty three NIFI images were selected. The PGs were recorded by NIFI and marked with artificial intelligence (AI) model. The recognition rate for PGs was calculated. Analyze the differences between surgeons of different years of experience and AI recognition, and evaluate the diagnostic and therapeutic efficacy of AI model.

RESULTS

Our model achieved 83.5% precision and 57.8% recall in the internal validation set. The visual recognition rate of AI model was 85.2% and 82.4% on internal and external sets. The PG recognition rate of AI model is higher than that of junior surgeons (p < 0.05).

CONCLUSIONS

This AI model will help surgeons identify PGs, and develop their learning ability and self-confidence.

Future Directions in the Treatment of Thyroid and Parathyroid Disease

The surgical management of thyroid and parathyroid disease has evolved considerably since the era of Theodor Kocher. We review the current trends in thyroid and parathyroid surgery concerning robotic surgery for remote access, the use of parathyroid autofluorescence detection technology to aid in the prevention of hypocalcemia as well as the use of thermal ablation to target thyroid nodules in a minimally invasive way. We also discuss how artificial intelligence is being used to improve the workflow and diagnostics preoperatively as well as for intraoperative decision-making. We also discuss potential areas where future research may enhance outcomes.

The utility of near infrared autofluorescence imaging for detecting small bowel carcinoid tumors in comparison to DOTATATE PET: A pilot study

BACKGROUND

Small bowel carcinoid (SBC) primary tumors can be multifocal in 40%-55% of patients and challenging to detect. Near infrared autofluorescence (NIRAF) is used for detection of parathyroid glands. It is unknown if this technology can be used to identify SBCs and how it would compare with current imaging modalities.

METHODS

This was a prospective institutional review board-approved pilot study of three patients undergoing resection of SBCs. NIRAF was used to image SBCs and mesenteric lymph nodes intraoperatively and at back table. Findings were compared with preoperative imaging, surgical exploration and pathology. Statistics were performed using Mann-Whitney U test.

RESULTS

Eleven SBCs and 12 mesenteric lymph nodes were analyzed. All SBCs had a brighter focal autofluorescence (AF) signal compared to background. Normalized pixel intensity of SBCs was 2.2 (0.7) and normal small bowel 1.4 (0.6) (p < 0.0001). NIRAF was less accurate in detecting occult lymph node metastasis, but was superior to DOTATATE PET for detecting SBCs in two of three patients.

CONCLUSIONS

This preliminary report suggests that SBCs exhibit distinctly bright AF signals on NIRAF to create a contrast distinction from normal small bowel. This technology may have a utility as an adjunctive tool for intraoperative detection of occult SBCs.